EOS Data Products

Handbook

Volume 1

TRMM • Terra

Data Assimilation System
EOS Data Products

Handbook

Volume 1

TRMM

CERES

LIS

PR

TMI

VIRS

Terra

ASTER

CERES

MISR

MODIS

MOPITT

Data

Assimilation

System
Editors

Michael D. King

NASA Goddard Space Flight Center

Jim Closs

Sterling Spangler

Renny Greenstone

SSAI

Stephen Wharton

NASA Goddard Space Flight Center

Monica Myers

Decision Systems Tech, Inc.

Design and Production

Sterling Spangler

For Additional Copies:

EOS Project Science Office

Code 900

NASA/Goddard Space Flight Center

Greenbelt, MD 20771

eos.nasa.gov

Phone: (301) 867-2037

Internet: leon_middleton@ssaihq.com

NASA Goddard Space Flight Center

Greenbelt, Maryland

Printed April 2003

EOS Data Products

Handbook

Volume 1

Revised 2004

Acknowledgments

Special thanks are extended to the 

many individuals who provided 

information necessary for the 

completion of this revised first 

volume of the EOS Data Products 

Handbook. These include most 

prominently Stephen W. Wharton 

and Monica Faeth Myers, whose 

tremendous efforts brought about 

the original version of Volume 1, 

and the members of the science 

teams for each of the instruments 

covered in this volume.

Support for production of this 

document, provided by Tim 

Suttles, William Bandeen, and 

Steve Graham is also gratefully 

acknowledged.

Certain portions of the text in this 

document have been reprinted 

from the EOS Data Products 

Handbook Volume 2 (Parkinson 

and Greenstone, 2000).
Abstract

The EOS Data Products Handbook provides brief descriptions of the data 

products that will be produced from a range of missions of the Earth Observing 

System (EOS) and associated projects. This publication is an update of Volume 

1, originally published in 1997, which covers the Tropical Rainfall Measuring 

Mission (TRMM), the Terra mission (formerly named EOS AM-1), and the Data 

Assimilation System. Volume 2, published in 2000, covers the Active Cavity 

Radiometer Irradiance Monitor Satellite (ACRIMSAT), Aqua, Jason-1, Landsat 

7, Meteor 3M/Stratospheric Aerosol and Gas Experiment III (SAGE III), the 

Quick Scatterometer (QuikScat), and the Vegetation Canopy Lidar (VCL) missions. Volume 1 provides a list of products and an introduction and overview 

descriptions of the instruments and data processing, all introductory to the core 

of the book, which presents the individual data product descriptions, organized 

into topical chapters. The product descriptions are followed by five appendices, 

which provide contact information for the EOS data centers that are archiving 

and distributing the data sets, contact information for the science points of contact for the data products, references, acronyms and abbreviations, and a data 

products index.

I
Abstract I

Foreword IV

List of Data Products (Organized by Mission Name) 1

Introduction 11

Background 15

Instrument Descriptions and Data Processing Overviews 21

Radiance and Imagery Products 47

Precipitation and Atmospheric Humidity 75

Cloud and Aerosol Properties and Radiative Energy Fluxes 101

Lightning 137

Atmospheric Chemistry 143

Surface Temperatures of Land and Oceans, 

 Fire Occurrence, and Volcanic Effects 149

Vegetation Dynamics, Land Cover, and Land Cover Dynamics 159

Ocean Primary Productivity, Phytoplankton and Organic Matter 177

Snow and Ice Cover 193

Data Assimilation System 201

Appendix A: EOS Distributed Active Archive Centers 

 (DAACs) Contact Information 219

Appendix B: Points of Contact 220

Appendix C: References 227

Appendix D: Acronyms and Abbreviations 253

Appendix E: Data Products Index (Organized by Instrument Name) 259

Table of Contents

III

Chapter 1

Chapter 2

Chapter 3

Chapter 4

Chapter 5

Chapter 6

Chapter 7

Chapter 8

Chapter 9

Chapter 10

Chapter 11

Chapter 12

Chapter 13
Foreword

This is a revised version of the MTPE/EOS Data Products Handbook, Volume 1,

TRMM & AM-1, published in 1997. The document, EOS Data Products Handbook, 

Volume 1, TRMM and Terra, has been updated to provide you with current information 

on the Terra and TRMM products available through the EOS Data and Information 

System.

This document provides high-level descriptions of the science and instrument data sets 

being produced from the Tropical Rainfall Measuring Mission (TRMM), the Earth 

Observing System (EOS) satellite, Terra, and the Data Assimilation System (DAS). 

These data sets are providing essential information for science and policy research in 

the areas of global change and Earth system science.

The document is broken into 14 chapters. Corresponding data products are grouped 

within each of chapters 4 through 14. Each data products chapter provides a framework for understanding the science objectives as well as providing a thumbnail sketch 

of what the product is designed to do.

A list of all products by ID and Data Set Name begins in the following section. That 

list also contains the page number where you will find the corresponding Product 

Description and other information.

Additional information on these missions can be found in the 1999 EOS Reference 

Handbook: A Guide to NASAʼs Earth Science Enterprise and the Earth Observing System (King, M. D., and R. Greenstone, 1999), and on the EOS Project Science Office 

web site at eos.nasa.gov, where you will also find many of our documents in PDF.

 Michael D. King

 EOS Senior Project Scientist

Foreword Data Products Handbook - Volume 1 V
1

List of Data Products

(Organized by Mission Name)

TRMM

CERES

LIS

PR

TMI

VIRS

Terra

ASTER

CERES

MISR

MODIS

MOPITT

Data

Assimilation

System
2 Data Products Handbook - Volume 1 List of Data Products (Organized by Mission Name) 

LIS Background Product 1A LIS01 Radiance and 50

 Imagery Products

LIS Image Attributes 1B LIS02 Lightning 140

LIS Events Statistics 1B LIS03 Lightning 140

LIS Group Statistics 1B LIS04 Lightning 140

LIS Flash Statistics 2 LIS05 Lightning 140

LIS Area Statistics 2 LIS06 Lightning 140

LIS Flash Density 2 LIS07 Lightning 140

LIS 2.5° × 2.5° Equal Angle 2 LIS08 Lightning 140

 Monthly Grid

LIS 500 km × 500 km Equal 2 LIS09 Lightning 140

 Area Monthly Grid

LIS Orbit Attributes 3 LIS10 Lightning 140

LIS Threshold Attributes 3 LIS11 Lightning 140

LIS Browse Area 3 LIS12 Lightning 140

LIS Vector Statistics 3 LIS13 Lightning 140

LIS Metadata Description 3 LIS14 Lightning 140

LIS Summary Data 3 LIS15 Lightning 140

LIS Flash Rate Data 3 LIS16 Lightning 140

LIS Ephemeris Data 3 LIS17 Lightning 140

LIS Event Rate Sets 3 LIS18 Lightning 140

PR Radar Total Power, Noise, 1B PR 1B-21 Radiance and 51

 and Reflectivity PR 1C-21 Imagery Products

PR Rain Occurrence and Rain 2 PR 2A-23 Precipitation, 80

 Type and Bright Band Atmospheric Humidity

 Height and Atmospheric 

 Temperatures

PR Surface Cross-Section as 2 PR 2A-21 Precipitation, 79

 Function of Scan Angle Atmospheric Humidity

 and Atmospheric 

Temperatures

PR Range Profiles of Rain 2 PR 2A-25 Precipitation, 81

 and Water Content Atmospheric Humidity

 and Atmospheric 

Temperatures

Mission Instrument Data Set Proc. Prod. ID Chapter Page # Level

TRMM

TRMM

TRMM
List of Data Products (Organized by Mission Name) Data Products Handbook - Volume 1 3

Mission Instrument Data Set Proc. Prod. ID Chapter Page # Level

TRMM

TRMM

PR Monthly Accumulated Rainfall 3 PR 3A-25 Precipitation, 82

 & Vertical Structure, Monthly PR 3A-26 Atmospheric Humidity

 Combined Accumulated and Atmospheric 

 Surface Rainfall Temperatures

TMI Calibrated Brightness 1B TMI 1B-11 Radiance and 51

 Temperatures Imagery Products

TMI Surface Rainfall and 2 TMI 2A-12 Precipitation, 83

 Vertical Structure Atmospheric Humidity

 and Atmospheric

Temperatures

TMI Monthly Surface Rainfall 3 TMI 3A-11 Precipitation, 84

 Atmospheric Humidity

and Atmospheric

Temperatures

TRMM Instantaneous Radar Site 2A TRMM Precipitation, 89

 Rain Map 2A-53 Atmospheric Humidity

 and Atmospheric

Temperatures

TRMM Instantaneous Radar Site 2A TRMM Precipitation, 90

 Convective/Stratiform Map 2A-54 Atmospheric Humidity

 and Atmospheric

Temperatures

TRMM Instantaneous Radar Site 2A TRMM Precipitation, 91

 3-D Reflectivities 2A-55 Atmospheric Humidity

 and Atmospheric

Temperatures

TRMM Combined Surface Rainfall 2 TRMM Precipitation, 85

 Rate and Vertical 2B-31 Atmospheric Humidity

 Structure and Atmospheric

 Temperatures

TRMM Monthly Combined 3 TRMM Precipitation, 86

 Accumulated Rainfall 3B-31 Atmospheric Humidity

 and Vertical Structure and Atmospheric

 Temperatures

TRMM 1° Daily Combined Rainfall 4 TRMM Precipitation, 88

 and Monthly Combined 3B-42 Atmospheric Humidity

 Instrument Rainfall 3B-43 and Atmospheric

 Temperatures

TRMM 5-Day Site Rain Map 3 TRMM Precipitation, 92

 3A-53 Atmospheric Humidity

 and Atmospheric

Temperatures

TRMM
4 Data Products Handbook - Volume 1 List of Data Products (Organized by Mission Name) 

Mission Instrument Data Set Proc. Prod. ID Chapter Page # Level

TRMM 30-Day Site Rain Map 3 TRMM Precipitation, 93

 3A-54 Atmospheric Humidity

 and Atmospheric

Temperatures

TRMM Monthly 3-D Structure 3 TRMM Precipitation, 93

 3A-55 Atmospheric Humidity

 and Atmospheric

Temperatures

VIRS Calibrated Radiances 1B VIRS 1B-01 Radiance and 52

 Imagery Products

CERES Bi-Directional Scans Product 0,1 CER/BDS Radiance and 61

 Imagery Products

CERES ERBE-like Instantaneous 2 CER/ES-8 Cloud and Aerosol 106

 TOA Estimates Properties and 

 Radiative

Energy Fluxes

CERES ERBE-like Monthly 3 CER/ES-4, Cloud and Aerosol 107

 Regional Averages (ES-9) and CER/ES-9 Properties and

 ERBE-like Monthly Geographical Radiative

 Averages (ES-4) Energy Fluxes 

 

CERES Single Scanner TOA/ 2 CER/SSF Cloud and Aerosol 109

 Surface Fluxes and Clouds Properties and 

 Radiative

Energy Fluxes

CERES Clouds and Radiative 2 CER/CRS Cloud and Aerosol 111

 Swath Properties and 

 Radiative

Energy Fluxes

CERES Monthly Gridded Radiative 3 CER/FSW Cloud and Aerosol 112

 Fluxes and Clouds Properties and 

 Radiative

Energy Fluxes

CERES Synoptic Radiative Fluxes 3 CER/SYN Cloud and Aerosol 114

 and Clouds Properties and 

 Radiative

Energy Fluxes

CERES Monthly Regional Radiative 3 CER/AVG, Cloud and Aerosol 115

 Fluxes and Clouds (AVG) CER/ZAVG Properties and

 and Monthly Zonal and Global Radiative

 Radiative Fluxes and Clouds (ZAVG) Energy Fluxes

 

TRMM

& 

Terra

TRMM

& 

Terra

TRMM
List of Data Products (Organized by Mission Name) Data Products Handbook - Volume 1 5

Mission Instrument Data Set Proc. Prod. ID Chapter Page # Level

Terra

Terra

Terra

CERES Monthly Gridded TOA/ 3 CER/SFC Cloud and Aerosol 116

 Surface Fluxes and Clouds Properties and 

 Radiative

Energy Fluxes

CERES Monthly TOA/Surface 3 CER/ Cloud and Aerosol 118

 Averages SRBAVG Properties and

 Radiative

Energy Fluxes

ASTER Reconstructed, Unprocessed 1A AST01 Radiance and 54

 Instrument Data Imagery Products

ASTER Registered Radiance 1B AST03 Radiance and 55

 at Sensor Imagery Products

ASTER Brightness Temperature 2 AST04 Radiance and 55

 at Sensor Imagery Products

ASTER Browse Data-Decorrelation 2 AST06 Radiance and 56

 Stretch Product Imagery Products

ASTER Surface Reflectance and 2 AST07 Radiance and 57

 Surface Radiance AST09 Imagery Products

ASTER Digital Elevation Models 3 AST14 Radiance and 59

 (DEMs) Imagery Products

ASTER Polar Surface and Cloud 4 AST13 Cloud and Aerosol 105

 Classification Product Properties and

 Radiative Energy Fluxes

ASTER Surface Emissivity and 2 AST05 Surface Temperatures 152

 Surface Kinetic Temperature AST08 of Land and Oceans,

 Fire Occurrence, and

Volcanic Effects

MISR Reformatted Annotated 1A MIS01 Radiance and 63

 Product Imagery Products

MISR Radiometric Product 1B1 MIS02 Radiance and 64

 Imagery Products

MISR Geo-rectified Radiance 1B2 MIS03 Radiance and 65

 Product Imagery Products

MISR Ancillary Geographic 1B2 MIS10 Radiance and 66

 Product & 2 Imagery Products

MISR Ancillary Radiometric 1B1 MIS11 Radiance and 66

 Product & 2 Imagery Products
6 Data Products Handbook - Volume 1 List of Data Products (Organized by Mission Name) 

Mission Instrument Data Set Proc. Prod. ID Chapter Page # Level

Terra

Terra

Terra

MISR Top-of-Atmosphere (TOA)/ 2 & 3 MIS04 Cloud and Aerosol 120

 Cloud Product MIS06 Properties and

 MIS07 Radiative Energy Fluxes

MISR Aerosol and Surface 2 & 3 MIS05 Cloud and Aerosol 122

 Product MIS08 Properties and

 MIS09 Radiative Energy Fluxes

MISR Aerosol Climatology 2 & 3 MIS12 Cloud and Aerosol 123

 Product Properties and

 Radiative Energy Fluxes

MODIS Level 1A Radiance Counts 1A MOD01 Radiance and 67

 Imagery Products

MODIS Level 1B Calibrated, 1B MOD02 Radince and 67

 Geolocated Radiances Imagery Products

MODIS Geolocation Data Set 1B MOD03 Radiance and 68

 Imagery Products

MODIS Aerosol Product 2 MOD04 Cloud and Aerosol 124

 Properties and 

Radiative

Energy Fluxes

MODIS Total Precipitable Water 2 MOD05 Precipitation, 95

 Atmospheric Humidity

and Atmospheric

Temperatures

MODIS Cloud Product 2 MOD06 Cloud and Aerosol 127

 Properties and 

Radiative

Energy Fluxes

MODIS Atmospheric Profiles 2 MOD07 Precipitation, 98

 Atmospheric Humidity

and Atmospheric

Temperatures

MODIS Level 3 Atmosphere 3 MOD08 Cloud and Aerosol 128

 Products Properties and 

 Radiative

Energy Fluxes

MODIS Surface Reflectance; 2 MOD09 Vegetation Dynamics, 162

 Atmospheric Correction Land Cover, and 

 Algorithm Products Land Cover Dynamics

MODIS Snow Cover 2,3 MOD10 Snow and Ice 196

 Cover

MODIS Land Surface 2,3 MOD11 Surface Temperatures 154

 Temperature (LST) and of Land and Oceans,

 Emissivity Fire Occurrence, and

 Volcanic Effects
List of Data Products (Organized by Mission Name) Data Products Handbook - Volume 1 7

Mission Instrument Data Set Proc. Prod. ID Chapter Page # Level

 

MODIS Land Cover Type 3 MOD12 Vegetation Dynamics, 164

 Land Cover, and 

Land Cover Dynamics

MODIS Vegetation Indices 3 MOD13 Vegetation Dynamics, 166

 Land Cover, and 

Land Cover Dynamics

MODIS Thermal Anomalies – 2,3 MOD14 Surface Temperatures 156

 Fires of Land and Oceans,

 Fire Occurrence, and

Volcanic Effects

MODIS Leaf Area Index and Fraction 4 MOD15 Vegetation Dynamics, 167

 of Photosynthetically Active Land Cover, and

 Radiation – Moderate Resolution Land Cover Dynamics

MODIS Evapotranspiration 4 MOD16 Vegetation Dynamics, 169

 Land Cover, and

Land Cover Dynamics

MODIS Vegetation Production and 4 MOD17 Vegetation Dynamics, 171

 Net Primary Production Land Cover, and

 Land Cover Dynamics

MODIS Normalized Water-Leaving 2,3 MOD18 Radiance and 69

 Radiance Imagery Products

MODIS Pigment Concentration 2,3 MOD19 Ocean Primary 180

 Productivity,

Phytoplankton and

Organic Matter

MODIS Chlorophyll Fluorescence 2,3 MOD20 Ocean Primary 182

 Productivity,

Phytoplankton and

Organic Matter

MODIS Chlorophyll a Pigment 2,3 MOD21 Ocean Primary 184

 Concentration Productivity,

 Phytoplankton and

Organic Matter

MODIS Photosynthetically 2,3 MOD22 Radiance and 70

 Active Radiation (PAR) Imagery Products

MODIS Suspended Solids 2,3 MOD23 Ocean Primary 180

 Concentration Productivity,

 Phytoplankton and

Organic Matter

MODIS Organic Matter 2,3 MOD24 Ocean Primary 184

 Concentration Productivity,

 Phytoplankton and

Organic Matter

Terra

Terra

Terra
8 Data Products Handbook - Volume 1 List of Data Products (Organized by Mission Name) 

Mission Instrument Data Set Proc. Prod. ID Chapter Page # Level

MODIS Coccolith 2,3 MOD25 Ocean Primary 187

 Concentration Productivity,

 Phytoplankton and

Organic Matter

MODIS Ocean Water Attenuation 2,3 MOD26 Ocean Primary 180

 Coefficient Productivity, 

 Phytoplankton and

Organic Matter 

MODIS Ocean Primary Productivity 4 MOD27 Ocean Primary 189

 Productivity,

Phytoplankton and

 Organic Matter

MODIS Sea Surface Temperature 2,3 MOD28 Surface Temperatures 157

 of Land and Oceans,

 Fire Occurrence, and

Volcanic Effects

MODIS Sea Ice Cover and Ice 2,3 MOD29 Snow and Ice 198

 Surface Temperature Cover

MODIS Phycoerythrin 2,3 MOD31 Ocean Primary 190

 Concentration Productivity,

 Phytoplankton and

Organic Matter

MODIS Processing Framework 2 MOD32 Radiance and 72

 and Match-up Database Imagery Products

MODIS Cloud Mask 2 MOD35 Cloud and Aerosol 134

 Properties and 

Radiative

Energy Fluxes

MODIS Absorption Coefficients 2,3 MOD36 Ocean Primary 184

 Productivity,

Phytoplankton and

Organic Matter

MODIS Aerosol Optical Depth 2,3 MOD37 Radiance and 69

 Imagery Products

MODIS Clear-Water Epsilon 2,3 MOD39 Radiance and 73

 Imagery Products

MODIS Burn Scars 4 MOD40 Surface Temperatures 156

 of Land and Oceans,

Fire Occurrence, and

Volcanic Effects

Terra

Terra

Terra
List of Data Products (Organized by Mission Name) Data Products Handbook - Volume 1 9

 Mission Instrument Data Set Proc. Prod. ID Chapter Page # Level

MODIS Surface Reflectance BRDF/ 3 MOD43 Vegetation Dynamics, 172

 Albedo Parameter Land Cover, and

 Land Cover Dynamics

MODIS Vegetative Cover Conversion 4 MOD44A Vegetation Dynamics, 174

 and Vegetation Continuous MOD44B Land Cover, and

 Fields Land Cover Dynamics

MOPITT Geolocated Radiances 1B MOP 01 Radiance and 74

 Imagery Products

MOPITT CO Profile, CO Column, 2 MOP 02 Atmospheric 146

 and CH4 Column Data Chemistry

DAS Time-Averaged Single Level 4 D4FAXCLD, Data Assimilation 205

 Cloud Quantities D4LAXCLD System

DAS Time-Averaged Near Surface 4 D4FAXENG, Data Assimilation 206

 and Vertically-Integrated D4LAXENG System

 Quantities

DAS Time-Averaged 2-Dimensional 4 D4FAXLSM, Data Assimilation 207

 Surface Data D4LAXLSM System

DAS Time-Averaged Surface and 4 D4FAXSTR, Data Assimilation 208

 Top-of-the-Atmosphere D4LAXSTR System

 Stresses

DAS Time-Averaged Surface 3- 4 D4LAPCLD Data Assimilation 209

 Dimensional Cloud Quantities System

DAS Time-Averaged 3-Dimensional 4 D4LAPMOM Data Assimilation 210

 Wind Tendency Fields System

DAS Time-Averaged 3-Dimensional 4 D4LAPMST Data Assimilation 211

 Moisture Tendency Fields System

DAS Total Column Ozone 4 D4FAXCHM Data Assimilation 212

 D4LAXCHM System

DAS Instantaneous Near Surface 4 D4FAXMIS Data Assimilation 213

 and Vertically-Integrated State D4LAXMIS System

 Variables

DAS Ozone Mixing Ratio 4 D4FAPCHM Data Assimilation 215

 D4LAPCHM System

DAS

Terra
10 Data Products Handbook - Volume 1 List of Data Products (Organized by Mission Name)

Mission Instrument Data Set Proc. Prod. ID Chapter Page # Level

DAS

DAS Instantaneous 3-Dimensional 4 D4FAPMIS Data Assimilation 216

 State Variables D4LAPMIS System

DAS Time-Averaged 3-Dimensional 4 D4LAPTMP Data Assimilation 217

 Temperature Tendency Fields System 

DAS Time-Averaged 3-Dimensional 4 D4LAPTRP Data Assimilation 218 

 Eddy-Diffusivity and Cloud System

 Mass Flux Fields
11

Chapter 1

Introduction

TRMM

CERES

LIS

PR

TMI

VIRS

Terra

ASTER

CERES

MISR

MODIS

MOPITT

Data

Assimilation

System
12 Data Products Handbook - Volume 1 Introduction 

Introduction

Overview

Volume 1 of the EOS Data Products Handbook 

(DPH) provides a brief description of the science data 

products that are available from the Earth Observing 

System Data and Information System (EOSDIS). The 

objective of the DPH is to promote a broader understanding of how the EOS data products contribute to 

science research in the understanding, analysis, and 

monitoring of global climate change. This volume 

describes data products that are produced from instruments onboard the Tropical Rainfall Measuring 

Mission (TRMM) satellite, the Earth Observing 

System (EOS) Terra satellite (formerly AM-1), as 

well as products developed by the Data Assimilation Office.

 The information in this document was compiled 

from many sources: principally, the Algorithm 

Theoretical Basis Documents (ATBDs), which were 

written by the science teams; abstracts and information from the product generation engineering effort 

known as the Ad Hoc Working Group on Production 

(AHWGP); and preliminary information compiled by 

the Science Processing Support Office (SPSO) of the 

Earth Science Data and Information System (ESDIS) 

Project. The data descriptions in this reference have 

been reviewed by the science team contacts for accuracy. Readers should be aware that this reference is 

only the “tip of the iceberg” of information available 

on the EOS data products.

Volume 1 Organization

Chapter 1 provides a roadmap through the document 

itself. Chapter 2 provides additional background on 

Earth Science Enterprise (ESE) mission objectives, a 

table of key characteristics for the TRMM and Terra 

instruments, and information on the EOSDIS and the 

EOS Distributed Active Archive Centers (DAACs). 

Chapter 3 provides an overview (for each instrument) 

of the process by which the data products are derived 

from the instrument observations.

 Chapters 4 through 12 contain the data set descriptions. Chapter 4 covers imagery and raw instrument 

data sets, as well as a few instrument ancillary data 

sets that are the basis for numerous other data sets with 

broad application to Earth science research. Chapters 

5 through 12 emphasize the 24 science themes that 

have been identified as critical measurements of the 

EOS program. Chapter 13 contains information about 

the Data Assimilation Office (DAO), which develops 

research-quality 4-dimensional atmosphere-oceanland data assimilation products for EOS. 

 The purpose of this documentʼs grouping is to provide a first-order indication of the kinds of science 

research that the data products can be used to support. The science themes are intended both to focus 

upon the mission objectives during the early EOS 

time frame and to represent a subset of the overall 

Earth Science Enterprise science objectives, which 

encompass a larger number of platforms and hence 

have a much broader scope.

The data set description chapter titles are as follows:

• Radiance and Imagery Products;

• Precipitation, Atmospheric Humidity and Atmospheric Temperatures;

• Cloud and Aerosol Properties and Radiative Energy 

Fluxes;

• Lightning;

• Atmospheric Chemistry;

• Surface Temperature of Land and Oceans, Fire 

Occurrence, and Volcanic Effects;

• Vegetation Dynamics, Land Cover, and Land Cover 

Dynamics;

• Ocean Primary Productivity, Phytoplankton and 

Organic Matter;

• Snow and Ice Cover; and

• Data Assimilation System products.

Readers looking for derived geophysical data (e.g., 

Sea Surface Temperature) will be most interested in 

the data described in the topical sections (e.g., Surface 

Temperature of Land and Oceans), while readers interested in dealing with large-volume data sets, such 

as uncalibrated instrument counts and temperatures, 

calibrated radiances, and reflectivities can refer to the 

Radiance and Imagery section. 

 It is important to understand that the science 

themes given for the data products are by no means 

all-inclusive. The applications envisioned today for 

products will change as new uses are discovered 

and as the products themselves are improved. These 

improvements are likely to come through algorithm 

refinement, or enhancements to input data needed for 

product development. Many data sets are applicable 

to more than one theme. Where more than one application is appropriate, a listing of key science applications is provided for the data set within its product 

summary description.
Introduction Data Products Handbook - Volume 1 13

Data Product Description

The data set descriptions contain the following information (Level 1 and radiance products contain a 

subset of this information):

• product description;

• research and applications;

• data set evolution;

• suggested reading; and

• product summary information

– coverage

– resolution

– wavelengths (Level 1 products only)

– key science applications (optional)

– key (geophysical if applicable) parameters 

(Level 2, 3, 4 products)

– processing level and availability

– product type and production mode

– science team contact.

The Product Description contains summary information about what is contained in the data set and 

a few details about the resolution, derivation, and 

accuracy of the data. Research and Applications

briefly describes some of the science questions and 

applications for which the data set will be useful. 

Data Set Evolution provides information on precursor 

data sets and highlights ways in which the data sets 

improve upon current capabilities. Suggested Reading

provides several references for more information. An 

effort has been made to include more recent publications, which are generally available to the broader 

science community. 

 Please note that the terms data set and data product 

are used interchangeably throughout this document. 

The data described in this document are distributed 

through the EOSDIS, and the EOSDIS science processing support office has assigned EOS identifiers 

(EOS-IDs) for each data product. These EOS-IDs are 

used throughout this document. These product IDs 

should not be confused with ATBD numbers assigned 

by the Project Science Office to identify each unique 

Algorithm Theoretical Basis Document which may 

contain multiple data products. 

 Note that CERES products use instrument IDs 

which do not use a numbering scheme.

Product Summary Information

The product summary information contains an overview of the processing level, coverage, resolution, 

and contact information for each data set.

Processing Level – Data set processing level is 

referred to throughout this document. The definitions of processing levels are shown in the box 

below. Level 1 data (e.g., Volts, Counts, Brightness 

Temperatures, etc.) require knowledge, agreed-upon 

algorithms, and computer processing capability for 

conversion to Level 2. Level 2 data measure the 

biogeophysical properties of Planet Earth. When a 

satellite instrument/algorithm is relatively new, the 

Level 2 product is heavily used. When the instrument/

algorithm is mature, many users prefer the Level 3 

product. Level 3 (mapped/gridded) and Level 4 

Level 0 - Reconstructed unprocessed instrument/payload data at full resolution; any and all 

communications artifacts (e.g., synchronization frames, communications headers) removed.

Level 1A - Reconstructed unprocessed instrument data at full resolution, time-referenced, and 

annotated with ancillary information, including radiometric and geometric calibration coefficients and georeferencing parameters (e.g., platform ephemeris) computed and appended, 

but not applied, to the Level 0 data. 

Level 1B - Level 1A data that have been processed to sensor units (not all instruments have a 

Level 1B equivalent).

Level 2 - Derived geophysical variables at the same resolution and location as the Level 1 

source data.

Level 3 - Variables mapped on uniform space-time grid scales, usually with some completeness and consistency.

Level 4 - Model output or results from analyses of lower level data (e.g., variables derived 

from multiple measurements).
14 Data Products Handbook - Volume 1 Introduction

(modeled) products can be used by interdisciplinary 

scientists to combine data from different areas of 

knowledge without necessarily having to know the 

details or undertake all of the processing that would 

otherwise be necessary (to process Level 1 to Level 

3 or 4). The availability of Level 3 and 4 data is a 

major advantage of EOSDIS in promoting data usage 

from outside the science field in which the data were 

generated. Some data sets are being produced and 

archived in both a Level 2 (unmapped) and Level 3 

(mapped and/or temporally sampled) form. 

 The differences in products allow researchers to 

choose products available at a particular level, enabling them to work in coarser resolution to model 

global processes and events or in finely detailed resolution which allows for investigations at targeted 

regions.

Coverage – The Terra satellite is in a Sun-synchronous polar orbit and views the globe, daytime 

and nighttime, at the same time every day. TRMM, 

however, is in a precessing, inclined orbit and views 

the Earth from 36°N latitude to 36°S latitude. Many 

instruments have large swaths and view the entire 

globe every day. Some of the higher resolution instruments have a small swath width and as such only view 

the complete Earthʼs surface over a period of several 

days or weeks. For each data set described in this 

reference, a summary of the coverage characteristics 

(day/night, global/local, etc.) is provided.

Processing Level and Availability – Most of the 

data sets in this reference are EOS “standard data 

products.” This means the products will be routinely 

processed for every applicable data acquisition. The 

science teams have identified the best available 

algorithms and methods for producing these data 

sets, given current understanding of physics and instrument performance; these are the “at-launch” data 

products. Some products, such as seasonal products 

or products describing variance from seasonal norms, 

cannot be implemented until some time after launch. 

In these cases, the data sets are referred to as “postlaunch.” All Level 1 data sets are standard at-launch 

products, while some Level 2, 3, and 4 data sets were 

phased in after launch.

 Great improvements have been made in the derivation of geophysical data products in the first few 

years of satellite operation, and EOSDIS has been 

sized to enable reprocessing of standard products 

periodically as improved methods and instrument 

characterization are available.

Contacts For Further Information – To learn more 

about the data sets described in this guide, the ATBDs 

can be acquired from the EOS Project Science Office 

(PSO) website at eospso.gsfc.nasa.gov. In addition, 

a science team contact is listed for each data set described in this volume, and the phone, fax, and e-mail 

information for these investigators can be found in the 

EOS Directory, available from the PSO Web page.

Further Information Sources

The reader is referred to Volume 2 (Parkinson and 

Greenstone, 2000) for information about the data 

products from ACRIMSAT, Aqua, Jason-1, Landsat 

7, Meteor 3M/SAGE III, and QuikScat. The reader 

is further referred to the 1999 EOS Reference Handbook (King and Greenstone, 1999) for background 

on NASAʼs Earth Science Enterprise and for details 

on the entire EOS program, including its scope, purposes, individual mission elements and instruments, 

science teams, interdisciplinary science investigations, and educational outreach programs. Finally, 

the reader is referred to the Science Strategy for the 

Earth Observing System (Asrar and Dozier, 1994) 

and the EOS Science Plan (King, 1999) and its accompanying Executive Summary (Greenstone and 

King, 1999) for details on the science issues being 

addressed by the EOS program and the approaches 

being used.

Suggested Reading

(full references are listed in Appendix C)

Asrar, G., and J. Dozier, 1994.

Asrar, G., and R. Greenstone, 1995.

Greenstone, R., and M. D. King, 1999.

King, M. D., 1999.

King, M. D., and R. Greenstone, 1999.
15

Chapter 2

Background

TRMM

CERES

LIS

PR

TMI

VIRS

Terra

ASTER

CERES

MISR

MODIS

MOPITT

Data

Assimilation

System
16 Data Products Handbook - Volume 1 Background 

Background

The Earth Science Enterprise

NASAʼs Earth Science Enterprise (ESE) program 

provides measurement systems and research initiatives to further the acquisition and synthesis of 

environmental data to address scientific issues related 

to global change and Earth system science. ESE programs include basic scientific research, development 

of Earth and space-based observing systems, field 

campaigns and experiments, state-of-the-art data 

set development, and data access and distribution 

systems. The Earth Observing System is a major 

program within ESE. 

 Among other programs of particular relevance to 

EOS are the New Millennium Program (NMP), the 

Earth System Science Pathfinder (ESSP) program, 

and Airborne Science. NMP was conceived to develop and flight-validate breakthrough technologies 

that will reduce the cost of high-priority science 

missions of the 21st century while enhancing their 

scientific capability. For example, the first mission, 

NMP/EO-1 (Earth Observing-1) carries an Advanced 

Land Imager (ALI) and other advanced imaging systems, looking forward to future improved Landsat 

follow-on missions. The Earth System Science Pathfinder Program (ESSP) was instituted to provide for 

Principle-Investigator-led missions of approximately 

three years duration addressing unique scientific 

problems that will complement, but not duplicate, 

the scientific objectives of EOS missions. For example, the first ESSP mission, the Gravity Recovery 

and Climate Experiment (GRACE), was launched 

to produce more accurate gravitational field maps of 

the Earth that will have important implications with 

respect to many climatic variables. 

 The National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP) is a joint mission to extend key 

measurements in support of long-term monitoring 

of climate trends and global biological productivity, 

and also to provide risk reduction for new NPOESS 

payloads. NPP spacecraft and instrument concepts are 

intended to provide a bridge between NASAʼs Earth 

Observing System mission and NPOESS. 

 NPP will extend the measurement series being 

initiated with the MODIS instrument on EOS Terra 

and the AIRS, AMSU, and HSB instruments that are 

flying on EOS Aqua. MODIS-like measurements will 

be continued using the Visible Infrared Imaging Radiometer Suite (VIIRS). AIRS-like measurements will 

be obtained from the Cross-track Infrared Sounder 

(CrIS). The NPOESS Integrated Program Office 

(IPO) will provide both of these sensors. The AMSU 

and HSB measurement series will be extended by the 

Advanced Technology Microwave Sounder (ATMS), 

which will be provided by NASA. Thus, NPP will 

provide NASA with a continuation of global-change 

observations that include monitoring of atmospheric 

and sea-surface temperatures, humidity sounding, 

land and ocean biological productivity, and cloud 

and aerosol properties. 

 In addition, the NPP mission will provide the 

NPOESS program with early access to the next 

generation of operational sensors, thereby greatly 

reducing the risks incurred during the transition 

from DMSP and POES. This will permit testing of 

the advanced ground operations facilities and validation of sensors and algorithms while the current 

operational systems are still in place. This new system 

will provide nearly an order of magnitude more data 

than the current operational system. 

 Launch is planned for late 2006 into an 824 km 

orbit with a 10:30 a.m. equator crossing time at the 

descending node. The orbit altitude is forward compatible with the NPOESS, while the crossing time is 

backward compatible with the EOS Terra mission. 

With a five-year design lifetime, NPP will provide 

data past the planned lifetime of EOS Terra and EOS 

Aqua and through the expected launch of the first 

NPOESS spacecraft in 2009. 

 Throughout all of these activities, ESE carries 

a strong commitment to make high quality, well 

documented Earth science data easily available to 

the global community to further environmental and 

global change research needs. The most pressing issues for global change research and NASAʼs Earth 

Science program are described in the “Earth Science 

Enterprise Strategic Plan,” available at the following 

web site: www.earth.nasa.gov/visions/index.html

 Three companion documents, the “Earth Science 

Enterprise Research Strategy,” the “Technology Strategy,” and the “Applications Strategy” are available 

on the same web site.

The Earth Observing System

The Earth Observing System (EOS) is a series of 

Earth-orbiting satellites which will provide global 

observations of the land surface, atmosphere, and 

oceans over 15 years or more, with the first satellites 

having been launched in the late 1990s. Complete 

information and details on the satellites and instruments included in EOS can be found in the 1999 EOS 
Background Data Products Handbook - Volume 1 17

Reference Handbook. This document can be directly 

accessed at:

eospso.gsfc.nasa.gov/eos_homepage/for_scientists/

data_products/refbook1999.php. 

EOS is complemented by missions and instruments 

from international partners, including Japan, Brazil, 

the European Space Agency (ESA), and ESA member 

countries. For example, the Tropical Rainfall Mapping Mission (TRMM) is a joint NASA/Japanese 

mission. In addition, some instruments flown on EOS 

satellites are provided by international partners, such 

as the Advanced Microwave Scanning Radiometer 

for EOS (AMSR-E) from Japan, and the Humidity Sounder for Brazil (HSB), both flying on Aqua. 

Together, these NASA and international programs 

form the basis for a comprehensive International 

Earth Observing System (IEOS).

 The EOS satellites carry two classes of instruments: 

Facility Instruments supplied by NASA and the Ministry of Economy, Trade and Industry (METI)/National 

Space Development Agency of Japan (NASDA) in 

response to general mission requirements, and Principal Investigator (PI) Instruments selected through 

a competitive process and aimed at the focused 

research interests of the selected investigators. Two 

EOS instruments—Clouds and the Earthʼs Radiant 

Energy System (CERES) and Lightning Imaging 

Sensor (LIS)—are flown on the TRMM satellite, 

in addition to non-EOS instruments—Precipitation 

Radar (PR), TRMM Microwave Imager (TMI), and 

Visible Infrared Scanner (VIRS). However, data 

from all of the instruments on TRMM are part of 

the ESE research and data development program 

and are distributed through the EOSDIS archives. 

Consequently, this data product handbook includes 

descriptions of the data sets from all of the TRMM 

instruments. The Terra platform is in a polar orbit with 

a descending node equator crossing time of 10:40 a.m. 

Terraʼs sibling satellite, Aqua (described in Volume 2 

of the EOS Data Products Handbook), was launched 

in May, 2002, and is in a complementary orbit crossing the equator at 1:30 p.m. local time. The TRMM 

satellite is in an inclined, equatorial orbit and only 

views the Earth from roughly 36°N to 36°S. Most of 

the Terra, Aqua and TRMM instruments operate in 

both daylight and darkness. However, some of the 

optical instruments collect visible data over areas of 

darkness.

 The instruments have variable scanning and 

coverage characteristics. Depending on the swath 

width, some instruments provide global data daily, 

while some of the higher resolution instruments have 

smaller swaths and only cover a portion of the day. 

Within a repeat cycle of a few days, however, most 

of these instruments also provide global imaging. In 

the case of TRMM, the orbital processing is 30 days 

for the geographic and diurnal repeat, and for Terra 

and Aqua, the repeat cycle is roughly nine days.

EOS Validation

EOS manages a comprehensive Validation Program 

to verify geophysical measurements obtained by satellite sensors. The EOS Instrument Science Teams are 

responsible for algorithm validation and specification 

of the uncertainties in the high-level geophysical 

quantities derived from calibrated instrument measurements. Guidelines, policies, coordination, and 

the review process for EOS-wide validation activities 

are the responsibility of the EOS Validation Scientist 

within the EOS Project Science Office. Pre-launch 

activities include development and verification of 

algorithms and characterization of uncertainties resulting from parameterizations and their algorithmic 

implementation. Post-launch activities include refinement of algorithms and uncertainty estimates based 

on near-direct comparisons with correlative measurements and selected, controlled analyses or application implementations. Airborne measurements using 

specifically designed EOS instrument simulators and 

community airborne instruments also play an essential 

role in pre-launch and post-launch studies. 

 The Airborne Science program complements the 

Validation Program by providing aircraft capable of 

both in situ and remote sensing to perform scientific 

experiments that demonstrate the feasibility of new 

sensors before producing a space-based system, and 

validate the measurements of new satellite-borne 

sensors. Examples of recent airborne campaigns 

are studies of the Pacific tropospheric chemistry, the 

ecology of the Amazon region of Brazil, and biomass 

burning in southern Africa.

Science Data Sets

Data and data sets define mission success for the EOS. 

In more than 25 years of Earth observation, scientists 

have begun only recently to routinely use global data 

sets of moderate to coarse resolution (1-10 km) for 

global change research. It has taken computational 

advances, lowering of data cost, relaxation of data 

access restrictions, and proactive data set generation 

efforts (such as the Pathfinder programs of NOAA 

and NASA) to enable the research community to effectively use global, remotely sensed data sets. In the 

next decade, the EOS data sets and interdisciplinary 

investigations will advance the state-of-the-art use 
18 Data Products Handbook - Volume 1 Background 

of global Earth observation data to high to moderate 

resolution data sets (100-1000 m). These data sets 

present an unprecedented opportunity and challenge 

for the Earth science community.

 The data sets for global change research that are being produced from the TRMM and Terra instruments 

have been selected by NASA through peer review. 

Investigators developing these data sets may be part 

of instrument teams or interdisciplinary science investigation teams.

 The unprecedented science goals and volumes of 

these data sets drive several critical issues for the 

EOSDIS and its ground segment, which produces, 

archives, and distributes the data. The issues of 

producing and distributing these data sets fall into 

two basic categories: science and engineering. The 

science issues are being addressed in part through 

the development of Algorithm Theoretical Basis 

Documents (ATBDs) and the computation and engineering issues of data set production, archiving and 

distribution are being addressed by the EOS Data and 

Information System (EOSDIS) developers.

Algorithm Theoretical Basis Documents

Efforts defining the data setsʼ theoretical basis and 

science algorithms are taking place among the instrument and investigator teams under the coordinating direction of the EOS Senior Project Scientist. 

Each EOS instrument team for the Terra and TRMM 

platforms, as well as the Data Assimilation Office 

(DAO), has presented for peer review an ATBD which 

provides detailed descriptions of the data set science 

algorithm. These documents include:

• Overview and Technical Background;

• Science Rationale;

• Algorithm Theoretical Description;

– Physics of the Problem

– Mathematical Description

– Uncertainty & Errors

• Practical Considerations;

– Science Software Considerations

– Calibration and Validation

– Quality Control

– Data Dependencies

– Product Definition; and

• Implementation Schedule (pre- and post-launch).

Much of the information in this Data Products Handbook comes from the ATBDs, which are available 

over the Internet at the following address:

eospso.gsfc.nasa.gov/eos_homepage/for_scientists/

atbd/index.php.

EOS Data and Information System

Critical to the generation and distribution of the EOS 

data sets is the EOS Data and Information System 

(EOSDIS). The EOSDIS provides traditional flight 

operations, mission support, and data processing roles 

for EOS spacecraft and instruments and provides archiving and distribution services for EOS data and 

a variety of global change and Earth observation 

data.

 The EOSDIS is taking advantage of recent advances 

in computational hardware, systems engineering, and 

national and international networking capabilities to 

implement a system of unprecedented capacity, user 

service, and openness.

EOSDIS Distributed Active Archive

Centers

Key elements of EOSDIS are the Distributed Active 

Archive Centers (DAACs), the facilities responsible 

for producing, archiving, and distributing the data 

sets. The EOSDIS links these archives through highspeed networks and an open architecture to integrate 

and provide data services to the research community. 

“Open Architecture” means that the user community 

is able to access the system directly through its own 

customized interfaces as well as through standard 

data access interfaces.

 There are currently nine DAACs representing a 

wide range of Earth science disciplines and socioeconomic and educational applications. The geographic distribution of the DAACs is illustrated on 

the following page. Also refer to Appendix A, which 

provides access information for the DAACs. These 

institutions are custodians of EOS mission data, 

and ensure that data are easily accessible to users. 

Acting in concert, DAACs provide reliable, robust 

services to users whose needs may cross traditional 

discipline boundaries, while continuing to support the 

particular needs of their respective discipline communities. DAAC assignments were based primarily 

on the distribution of scientific expertise, institutional 

heritage, and capability. Each DAAC has a working 

group of users to provide recommendations on priorities for scientific data, levels of service, and the 

needed capabilities. The DAACs actively participate 

in the design, implementation, and operation of EOSDIS. 

 The EOSDIS Data Gateway provides information and ordering of data and documentation at the 

DAACs using a menu-driven interface. The service 

is open to the public with access to restricted data 

by authorization. The ordering service allows users 

to identify, browse, and then select data by time and 
Background Data Products Handbook - Volume 1 19

location, geophysical parameter, and processing 

level. The service is accessible via the internet at 

eos.nasa.gov/imswelcome. Access information for 

online and user support services for each of the 

DAACs is listed in Appendix A.

Data Set Distribution

The EOS data policy is to provide data to the global 

research community with few restrictions and at no 

cost or at most no more than the marginal cost of filling an order. Details of the EOS data policy can be 

found in the 1999 EOS Reference Handbook. EOS 

data are not copyrighted; however, it is requested that 

all researchers who publish data or results using EOS 

data sets include the following acknowledgment:

 Data used in this research include data produced 

through the funding of NASAʼs Earth Science Enterprise Earth Observing System Program. 

 In addition, for publications using EOSDIS-provided data the following acknowledgment should be 

used: 

 Data used in this research include data provided 

to the authors by the NASA-funded EOS Data and 

Information System archive at [insert name of archive 

center providing data].

Reproducibility and Longevity

Since global change studies require access to Earth 

observation data over decades, one of the key directives of the EOSDIS is that all data archived and 

distributed will be sufficiently documented such that 

researchers looking at data 10 or 20 years after the 

launch of the spacecraft will be able to understand and 

read the data. Related to this is the principle that all 

software, documentation, ancillary data, etc., be available to users so that any data set will be reproducible 

by anyone in the research community. In addition to 

ensuring easy and open access, this will aid in the 

scientific peer review and validation process.

Suggested Reading

Asrar, G., and R. Greenstone, Eds., 1995.

Asrar, G., and J. Dozier, 1994.

Asrar, G. et al., 2001.

Barry, R. G., and A. L. Varani, Eds., 1995.

King, M. D., and R. Greenstone, 1999.

Sellers, P. J. et al., 1995.

Distributed Active Archive Centers

�������

���������������

�������

�����

��������������

������������

����

��������������������������

��������������������������

��������

�����������������

���������������������

��������������������

����

�����������������

�������������

����������������������

�������

����������������������

�������������������

�����

����������

���������

���

������������������

�����������������

����

���������������������

���������
21

Chapter 3

Instrument Descriptions

and

Data Processing Overviews

TRMM

CERES

LIS

PR

TMI

VIRS

Terra

ASTER

CERES

MISR

MODIS

MOPITT
22 Data Products Handbook - Volume 1 Instrument Descriptions and Data Processing Overviews 

Overview

Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) has higher spatial resolution, but lower average duty cycle (8% day, 16% 

night). The Multi-Angle Imaging Spectroradiometer 

(MISR) is similar to MODIS in the 250-m bands. 

MODIS data can be used for atmospheric correction 

for ASTER and cloud screening for Clouds and the 

Earthʼs Radiant Energy System (CERES). Similarly, 

there are synergies between MISR and MODIS in 

characterizing aerosols over oceans. ASTER with its 

pointing, stereo mapping, and multi-spectral thermal 

infrared (IR) radiometer, provides synergism with 

MODIS and MISR, and acquires essential data for 

the study of volcanoes and surface climate. ASTER 

also provides simultaneous multispectral, high-resolution detail to support global mapping of surface 

vegetation by MODIS and MISR. Simultaneous data 

from ASTER are essential to understand the subpixel 

variability of MODIS and MISR data.

 The synergy provided by EOS instruments may 

be used to obtain and apply atmospheric corrections 

to allow quantitative, rather than qualitative, remote 

sensing science (such atmospheric attenuation and 

cloud-screening corrections are applicable to multiple sensors, MODIS cloud cover for CERES and 

MOPITT, MODIS total-water vapor for ASTER). 

Also, they may be used to validate data product algorithms and maintain instrument calibrations over a 

15-year period, leading to support of a broad range of 

interdisciplinary investigations describing the Earthʼs 

carbon, energy, and water cycles, and our impact on 

them.

 For information about the products developed by 

the Data Assimilation Office, see Chapter 13.

Suggested Reading

Earth Observing System: Science and Mission Requirements Working Group Report.

Hobish, M. K. et al., 1994.

King, M. D., ed., 1999.

Mission Orbit Type Altitude Equatorial crossing time Launch date

TRMM Non sun-synchronous, 402 km ----------- November 27, 1997

 36° inclination 

Terra Polar, sun-synchronous, 720 km 10:40 a.m. December 18, 1999

 98.1° inclination

Satellite missions covered in this volume

The EOS system is designed to foster interdisciplinary use of Earth science data sets in ways that 

have been previously impossible. The science strategy identified early for ESE was to group instruments 

to exploit their capabilities for synergism in surface 

imaging and in determining related properties of the 

atmosphere. Cross-calibration of the instruments and 

validation of their resulting data products further enhance this synergism. Through EOSDIS, efforts to 

provide better data utilization include standardization 

of formats and media, consistency in standards and 

processing, and a platform of standard analysis tools 

to support investigation of the data.

 Within the full EOS instrument suite, sets of instruments complement one another to produce desired 

observations. In some instances, certain instrument 

suites must generate correlative data; therefore, they 

must make their measurements simultaneously. For 

example, measurement of ocean circulation requires 

global altimeter data, precise orbit determination, 

and water vapor amounts. These observations also 

contain signals resulting from ocean tide deformation, 

atmospheric pressure and ocean surface interactions, 

and sea-level change, including long-term average sea 

level as well as interannual hemispherical variations. 

Such sets of observations dictate some minimum payload groupings.

 The EOS science program combines the measurements from the assembled suite of instruments, using 

some instruments to refine the measurements of others 

or to provide data products derived from more than 

one instrument. This strategy permits the extraction 

of parameters that a single instrument cannot measure 

reliably. This flow of data, the product of one data set 

used as input to another, is critical to understanding 

the complex interrelationships of the Earthʼs system. 

Global and regional data assimilation models that 

function as part of EOSDIS use the observations from 

the EOS platforms to provide continuous calculations 

of the principal fluxes between components.

 All EOS instruments have their own primary 

characteristics, and all fit together to complement 

each other. For example, compared with Moderate 

Resolution Imaging Spectroradiometer (MODIS), 
Instrument Descriptions and Data Processing Overviews Data Products Handbook - Volume 1 23

Satellite

TRMM 

TRMM 

TRMM

TRMM &

Terra

TRMM

Terra

Terra

Terra

Terra

Instrument

PR – Precipitation Radar 

TMI – TRMM Microwave

Imager

VIRS – Visible Infrared 

Scanner

CERES – Clouds & the 

Earth’s Radiant Energy 

System

LIS – Lightning Imaging 

Sensor 

ASTER – Advanced

Spaceborne Thermal

Emission & Reflection

Radiometer

MISR – Multi-angle Imaging

Spectroradiometer

MODIS – Moderate Resolution Imaging Spectroradiometer

MOPITT – Measurements 

of Pollution in The 

Troposphere

Description

An electronically scanning radar operating at 13.8 GHz; 

4.3 km instantaneous field-of-view at nadir; 220-km 

swath. Provided by NASDA (Japan).

A nine-channel conical scanning passive microwave imager making measurements from 10 to 85 GHz, 37 to 4.6 

km resolution respectively, covering 760 km swath.

A five-channel cross-track imaging radiometer (0.62, 

1.63, 3.78, 10.83, and 12.03 μm) with nominal 2-km 

resolution at nadir and 720-km swath.

A three-channel radiometer (0.3 to > 50 μm, 0.3 to 

5 μm, 8 to 12 μm). TRMM has one cross-track scanning 

radiometer and Terra has two instruments, one operating 

in cross-track mode and one in rotating azimuth plane 

mode. The spatial resolution at nadir is 10 km from the 

TRMM orbit and 20 km from the highest Terra orbit.

Staring telescope/filter imaging system (0.777 μm) with 

5-km spatial resolution and 2-msec temporal resolution 

over an imaging area of 600 × 600 km.

A three-radiometer sensor package with three vis/nearIR, six shortwave, and five thermal infrared channels with 

15, 30, and 90-m resolution, respectively, and a 60-km 

swath. Provided by the Japanese Ministry of Economy, 

Trade and Industry (METI).

Thirty-six channel instrument; nine pushbroom cameras 

with discrete view angles (to ± 70°) in four spectral bands 

(0.443 to 0.865 μm) with resolution of 275 m to 1.1 km.

Thirty-six channel imaging radiometer (0.4 μm to

14.5 μm) with 250-m, 500-m, or 1-km resolution and 

2,300-km swath width.

Eight-channel cross-track scanning gas correlation 

radiometer operating at three wavelengths (2.2, 2.3, and 

4.7 μm).

Coverage characteristics of the instruments on TRMM and Terra
24 Data Products Handbook - Volume 1 • Instrument Descriptions and Data Processing Overviews 

TRMM Mission

TRMM

Tropical Rainfall Measuring Mission

TRMM is a joint NASA/Japan satellite designed 

specifically to monitor rainfall and its associated latent heating in the tropics and subtropics. The total 

rainfall and its distribution are important, because the 

atmosphere gets three-fourths of its heat energy from 

the release of latent heat in the process of precipitation. The horizontal and vertical location at which 

this energy is released affects the weather around the 

world. Despite its fundamental importance to global 

climate, the total rainfall in the tropics is not well 

known, and the vertical distribution of latent heating can only be estimated imprecisely. Although the 

sensors on TRMM have utility beyond the primary 

rainfall parameters, the TRMM Science Team has 

defined and developed a set of “standard products” 

that are critical to monitoring rainfall and its vertical 

structure. These standard products are processed by 

the TRMM Science Data and Information System 

(TSDIS).

 The figure on the following page shows how the 

various satellite sensors and algorithms interact in order to provide the TRMM satellite standard products. 

The algorithms are coded by members of the TRMM 

science team who have the responsibility to develop 

these products. As an example, the passive microwave 

(TMI) team has the responsibility for generating code 

to produce TMI calibrated brightness temperatures 

(1B-11), TMI rainfall structure products (2A-12), and 

TMI monthly surface rainfall maps (3A-11). This type 

of in-team algorithm development can be seen for 

each of the three instrument teams. 

 A key feature of TRMM is the combined algorithms. These algorithms include the “TRMM Combined Algorithm,” which refers to an algorithm that 

combines TRMM sensor measurements; and “TRMM 

& Other Sensors,” which refers to algorithms that take 

the best rain estimates from TRMM and use them to 

calibrate other available data, in order to reduce the 

sampling bias.

 The TRMM mission has identified four main 

ground validation (GV) sites, which include groundbased radars and their associated rain gauge networks. 

These sites include Southern Florida, Australia (Darwin), Southeastern Texas, and the Marshall Islands 

(Kwajalein Atoll). GV data are processed at Goddard 

Space Flight Center in cooperation with the TRMM 

GV team. A flow diagram of these products and how 

they interact is shown in the second figure. All GV 

products for the different sites are processed using 

the same algorithms developed by the TRMM GV 

Team.

 The TRMM mission recognizes that new sensor data often lead to significant improvements in 

interpretation of remotely sensed data and quality 

of products. To ensure that any improvements in 

the products are quickly passed on to the research 

community, TRMM reprocesses data periodically as 

better algorithms or calibration procedures become 

available. To provide the stable long time series of 

products needed for Earth system research, the entire 

TRMM data set is reprocessed each time starting from 

day one of the mission.

 On August 22, 2001, the spacecraft orbit was 

boosted from 350 km to 402 km to conserve its 

remaining fuel and extend the missionʼs life into the 

2005-2007 time frame. Note that the instrument swath 

sizes increased by about 15 percent with the altitude 

increase, but there are no other significant changes 

to the data products.

TRMM

TRMM Home Page: trmm.gsfc.nasa.gov
Instrument Descriptions and Data Processing Overviews • TRMM Data Products Handbook - Volume 1 25

TRMM Instrument Algorithms and Products

TRMM Mission

VIRS

Level 1A

Level 1 Radiances

1B-01

VIRS Calibrated

Radiances

TMI

Level 1A

PR 

Level 1A

3A-11

TMI

Surface Rainfall

3A-25

PR Accumulated

Rainfall and Vertical

Structure

3A-26

PR Accumulated

Surface Rainfall

3B-43

Monthly Combined 

Instrument 

Rainfall

Level 2 Instantaneous

Geophysical Parameters

1B-11

TMI Calibrated

Brightness

Temperatures

1B-21

PR Radar Total 

Power and Noise

1C-21

PR

Reflectivity

Level 3 TRMM

Products

30 day 5 ̊

×5 ̊

Level 4 TRMM and Other

Satellite Products

5 day 1 ̊×1 ̊

NASA - TSDIS / NASDA - EOC

2A-21

PR Cross Section as 

Function of Scan Angle

2A-23

PR Rain Occurrence/

Type & Bright

Band Height

2A-12

TMI Surface Rainfall

and Vertical Structure

2B-31

Combined Surface

Rainfall Rate and 

Vertical Structure

2A-25

PR Range Profiles

of Rain and

Water Content

3B-31

Combined Accumulated

Rainfall and Vertical

Structure

3A-44

GPI

3A-46 SSM/I

Gridded Rain

3A-45 GPCP

Gridded Rain

3B-42

1o Daily Combined

Rainfall
26 Data Products Handbook - Volume 1 • Instrument Descriptions and Data Processing Overviews 

TRMM Mission

TRMM

Interaction of TRMM Ground-Based Radar Validation Products Level 1 Radiances Level 2 Instantaneous Geophysical Parameters Level 3 TRMM Products 30 day 2 km Level 4 TRMM and Other Satellite Products 5 day 2 km

GV Sites

Radar

Tapes

1B-51

Radar

Reflectivities

1C-51

Quality Controlled

Radar Reflectivities

2A-54

Radar Site

Convective/

Stratiform Map

2A-52

Rain

Existence

3A-55

Monthly

3-D Structure

3A-54

30 Day Site

Rain Map

2A-55

Radar Site 3-D

Reflectivities

3A-53

5 Day Site

Rain Map

2A-53

Radar Site

Rain Map
Instrument Descriptions and Data Processing Overviews • TRMM / LIS Data Products Handbook - Volume 1 27

LIS

 Lightning Imaging Sensor

The Lightning Imaging Sensor (LIS) is a calibrated, 

optical imager designed to measure the global 

incidence of lightning, and to investigate its correlation with rainfall and its relationship with the 

global electric circuit. Conceptually, LIS is a simple 

device, consisting of a staring imager optimized to 

locate both intracloud and cloud-to-ground lightning 

with storm-scale resolution over a large region of the 

Earthʼs surface, to mark the time of occurrence, and to 

measure the radiant energy. It was designed to monitor individual storms within its field-of-view (FOV) 

for more than 80 seconds, which is long enough to 

estimate the flashing rate of active storms. LIS was 

launched on TRMM in November 1997 and has 

performed flawlessly since that time. It has provided global maps of tropical lightning activity with 

location accuracies of greater than 5 km.

 The LIS design uses an expanded-optics 100 ̊ FOV 

lens combined with a narrow-band width (1.0 nm) 

interference filter that focuses the image on a small, 

high-speed, charge-coupled-device (CCD) focal 

plane. The signal is read out from the focal plane 

into a real-time data processor for event detection 

and data compression. The unusual characteristics 

of the sensor design result from the requirement to 

detect weak lightning signals during the day when 

the background illumination, produced by sunlight 

reflecting from the tops of clouds, is much brighter 

than the illumination produced by the lightning.

 A combination of four methods is used to take advantage of the significant differences in the temporal, 

spatial, and spectral characteristics between the lightning signal and the background noise. First, spatial 

filtering is used to match the instantaneous FOV of 

each detector element in the LIS focal-plane array to 

the typical cloud-top area illuminated by a lightning 

event (about 5 km). Second, spectral filtering is applied, using a narrow-band interference filter centered 

about the strong singly ionized oxygen emission line 

(OI [1]) multiplet in the lightning spectrum at 777.4 

nm. Third, temporal filtering, using very short CCD 

integration times (2 milliseconds), is applied to further suppress the background noise.

 The lightning pulse duration is of the order of 400 

microseconds, whereas the background illumination 

tends to be constant on a time scale of seconds. Hence, 

the lightning signal-to-noise ratio improves as the 

CCD integration time approaches the pulse duration 

until a significant number of pulses start to be split 

between successive frames. The two-millisecond integration time helps maximize lightning detectability. 

Finally, a modified frame-to-frame background subtraction is used to remove the slowly varying background signal from the raw data coming off the LIS 

focal plane. If after background removal, the signal 

for a given pixel exceeds a specified threshold, that 

pixel is processed as if it contains a lightning event. 

 Since its launch, LIS investigations have furthered 

our understanding of processes related to, and underlying, lightning phenomena in the Earth/atmosphere 

system. These processes include the amount, distribution, and structure of deep convection on a 

global scale, and the coupling between atmospheric 

dynamics and energetics as related to the global distribution of lightning activity. These investigations 

have contributed to a number of important EOS 

mission objectives, including cloud characterization 

and hydrologic cycle studies. LIS studies have demonstrated that lightning activity is closely coupled 

to storm convection, dynamics, and microphysics, 

and can be correlated to the global rates, amounts, 

and distribution of ice phase precipitation, to the release and transport of latent heat, and to the natural 

production of NOx. LIS standard products include 

the locations, times of occurrence, and intensities of 

lightning events.

Key LIS Facts

LIS Home Page: thunder.msfc.nasa.gov

EOS-funded instrument, launched on TRMM 

November 27, 1997

Staring telescope/filter imaging system that 

detects the rate, location, and radiant energy 

of lightning flashes

Investigates the distribution and variability of 

lightning over the Earth with storm-scale spatial 

resolution

80-90% detection efficiency under both day and 

night conditions using background remover and 

event processor

TRMM Mission
28 Data Products Handbook - Volume 1 • Instrument Descriptions and Data Processing Overviews 

TRMM Mission

LIS Data Flow (continued next page)

TRMM / LIS

Con

version to

Level 1

Conversion to Level 1 Data

Pixel Space Filtering

Ephemeris Conversion Routine

Raw

Inputs

Ephemeris

Con

version

Raw

Event Data

Raw

Background

Data

Summary

of Raw

Data

Raw

Data

Statistics

Raw Data

Threshold

Changes

Raw

Data

Errors

TRMM/LIS

Raw Data

TRMM/LIS

Ephemeris/

Attitude Data

Geo-Location of Pixel Space Data

Geo-Location Based Filtering

Pixel

Filtered

Event 

Data

Pixel

Filtered

Background

Data

Summary

of Pixel

Filtered

Data

Filtered

 Data

Threshold

Changes

Pixel

Filtered

Data

Errors

Pixel

Filtered

Data

Statistics

GeoLocation

Event 

Data

Summary

of GeoLocation

Data

GeoLocation

Data

Errors

GeoLocation

Data

Statistics

GeoLocation

Background

Data

GeoLocation

Filtered

Event 

Data

GeoLocation

Filtered

Data

Statistics

Summary

of GeoLocation

Filtered

Data

1 Second

Ephemeris

Data

Equatorial

Crossing

Times

Pixel Space

Filtering Geo-Location

KEY: Data Conversion Routine

Geophysical

Component

Summary

Component

QA/QC

Component Data Flow

Geo-Location

Based Filtering

1 2

F

B C

G

D

A E H
Instrument Descriptions and Data Processing Overviews • Data Products Handbook - Volume 1 29

LIS Data Flow (continued)

TRMM / LIS 

TRMM Mission

Viewtime

Calculation

TRMM/LIS

Ephemeris

Attitude Data

HDF File

Components

KEY: Data Conversion Routine

Geophysical

Component

Summary

Component

QA/QC

Component Data Flow

Equatorial

Crossing

Times

1 2

A B C F D E G H

Summary

of GeoLocation

Filtered

Data

GeoLocation

Filtered

Data

Statistics

True Instrument FOV Calculations

GeoLocation

Filtered

Event

Data

1 second

Ephemeris

Data

GeoLocation

Background

Data

GeoLocation

Data

Statistics

Filtered

 Data

Threshold

Changes

Point-byPoint

Viewtime

Data

Total

Viewtime

Data

GeoLocation

Filtered

Viewtime

Statistics

Orbit Division/HDF File Creation Routines

Orbit Data

Orbit Summary

One-Second Data

Contains:

 • Ephemeris

 • Operational

 • Event Rates

Point Data

Information

Point Data Summary

Lightning Data

• Areas

• Flashes

• Groups

• Events

Viewtime

Granules

Background

Image

Data

Orbit

Summary

Image

(RIS-8)

Orbit Data

Orbit Summary

Background

Image

Information

Background

Image

Data

Background

Image

Data
30 Data Products Handbook - Volume 1 • Instrument Descriptions and Data Processing Overviews 

Terra Mission

ASTER

Advanced Spaceborne Thermal Emission and 

Reflection Radiometer

ASTER is a Facility Instrument provided for the EOS 

Terra platform by the Japanese Ministry of Economy, Trade and Industry (METI). It provides high 

spatial resolution (15-90 m) multispectral images 

of the Earthʼs surface and clouds in order to better 

understand the physical processes that affect climate 

change. ASTER provides data at a scale that can be 

directly related to detailed physical processes. These 

data bridge the gap between field observations and 

data acquired by coarse spatial resolution instruments 

such as MODIS and MISR, and between local-process models and regional models.

 Clouds are one of the most important variables in 

the global climate system. With its high spatial-resolution, broad spectral coverage, and stereo capability, 

ASTER provides essential measurements of cloud 

amount, type, spatial distribution, morphology, and 

radiative properties. ASTER data are also used for 

long-term monitoring of local and regional changes 

on the Earthʼs surface, which either lead to, or are in 

response to, global climate change (e.g., land use, 

deforestation, desertification, lake and playa water 

level changes) and other changes in vegetation communities, glacial movement, and volcanic processes. 

ASTER provides radiative (brightness) temperature, 

and the multispectral thermal infrared (TIR) data 

can be used to derive surface kinetic temperature 

and spectral emissivity. Radiative temperature is an 

element in the surface heat balance. Surface kinetic 

temperature can be used to determine elements of 

surface process models, sensible heat flux, latent heat 

flux, and ground heat conduction. Surface temperatures are also related to thermophysical properties 

(such as thermal inertia), vegetation health, soil 

moisture, temporal land classification (e.g., wet vs. 

dry, vegetated vs. bare soil) and evapotranspiration. 

 ASTER operates in three visible and near-infrared 

(VNIR) channels between 0.52 and 0.86 μm, with 

15-m resolution; six shortwave infrared (SWIR) 

ASTER Data Product Architecture

Terra / ASTER

�����

1�

���� ���� ���

���������������

�����

���

�����������������

������������������

�����������������

������������������

�����������������

������������������

����������������������

������������������

����������������������

������������������

����������������������

������������������

�������������������������������������������

��������������������������������������

����������

�����������

��������������������������������

������������������������

�����������

�������������

����������

�������������������������

����������������������

����������������

�����������

�����

���

�����

���

�����

������

����� 1� ������

�

�����

����

���

����

����

����

���

1

�����

����

���

����

����

����

1

�������

1��������������������������������������������������������������������������������������������������������������

���������������������������������������������������

����������������������������������������������������������� ������
Instrument Descriptions and Data Processing Overviews • Data Products Handbook - Volume 1 31

channels between 1.60 and 2.43 μm, with 30-m 

resolution; and five TIR channels between 8.13 and 

11.65 μm, with 90-m resolution. The instrument 

acquires data over a 60-km swath whose center is 

pointable (cross-track ± 8.55° in the SWIR and TIR, 

with the VNIR pointable out to ± 24°). An additional 

VNIR telescope (aft pointing) covers the wavelength 

range of Channel 3. By combining these data with 

those for Channel 3, stereo views can be created, 

with a base-to-height ratio of 0.6. ASTERʼs pointing 

capabilities are such that any point on the globe is accessible at least once every 16 days in all 14 bands on 

average every 4 days in the three VNIR channels.

 ASTER data products exploit combinations of 

VNIR, SWIR, and TIR for cloud studies, surface 

mapping, soil and geologic studies, volcano monitoring, and for surface temperature, emissivity, and 

reflectivity determination. VNIR and SWIR bands 

are used for investigation of land use patterns and 

vegetation, VNIR and TIR combinations for the 

study of coral reefs and glaciers, and VNIR for 

digital elevation models (DEMs). TIR channels are 

used for study of evapotranspiration, and land and 

ocean temperature. The stereoscopic capability yields 

local topography, cloud structure, volcanic plumes, 

and glacial changes.

 Since launch in 1999, the ASTER instrument 

has performed exceptionally well. No operational 

anomalies have occurred. Analyses of the data have 

revealed several characteristics not accounted for 

prior to launch. Most of these have been addressed, 

and software corrections implemented in the standard 

data processing algorithms. There remain a small 

number of factors that the ASTER Science Team is 

still addressing, and developing corrections. 

1) Optical cross-talk in the SWIR spectrometer results 

in anomalous signal leaking into adjacent bands; this 

is most pronounced for energy falling on detectors for 

Band 4, and a small fraction reflecting onto detectors 

for Bands 5 and 9. The effect has been characterized, 

and software is being tested to correct the problem. It 

is anticipated that a revised standard higher level data 

product will incorporate the corrections. 

2) A scratch has been detected on the filter in the 

SWIR detector package; it manifests itself as a 

squiggly line in harshly processed data. A software 

correction is being tested, and will be incorporated 

in the Level 1 software algorithms.

3) An error in the geo-location information in the 

metadata was found: there is a fixed error for the earth 

rotation angle in the coordinate transformation from 

the earth inertial frame to the earth fixed frame. The 

error is very small at mid-latitudes. A correction table 

will be made available to users to correct this error 

in the near future, and a modification of the Level 1 

production software is planned.

Suggested Reading

Abrams, M., 2000.

Iwasaki, A. et al., 2002.

Yamaguchi, Y. et al., 2001.

ASTER Users Handbook, Version 2, 

asterweb.jpl.nasa.gov

ASTER Level 1 Users Guide, www.science.aster.er

sdac.or.jp/users/defaulte.htm

Key ASTER Facts

ASTER Home Page: asterweb.jpl.nasa.gov

Launched on Terra December 18, 1999

Heritage: JERS-1 and Landsat

Imaging radiometer

Provides high-resolution images of the land 

surface, water, ice, and clouds

Earth-orbit stereo capability

14 multispectral bands from visible through 

thermal infrared

Terra / ASTER

ASTER

Terra Mission
32 Data Products Handbook - Volume 1 • Instrument Descriptions and Data Processing Overviews 

TRMM & Terra Missions

TRMM and Terra / CERES

CERES

Clouds and the Earth’s Radiant Energy System

The U.S. Global Change Research Program classifies 

the role of clouds and radiation as its highest scientific 

priority (CEES, 1994). There are many excellent summaries of the scientific issues (IPCC, 1992; Hansen 

et al., 1993; Ramanathan et al., 1989; Randall et al., 

1989, Wielicki et al., 1996) concerning the role of 

clouds and radiation in the climate system. These 

issues naturally lead to a requirement for improved 

global observations of both radiative fluxes and cloud 

physical properties. The CERES Science Team, in 

conjunction with the EOS Investigators Working 

Group representing a wide range of scientific disciplines from oceans, to land processes, to atmosphere, 

has examined these issues and proposed an observational system with the following objectives:

1) For climate-change analysis, provide a continuation of the Earth Radiation Budget Experiment 

(ERBE) record of radiative fluxes at the top of the 

atmosphere (TOA), analyzed using the same algorithms that produced the existing ERBE data.

2) Double the accuracy of estimates of radiative 

fluxes at TOA and the Earthʼs surface. 

3) Provide the first long-term global estimates of the 

radiative fluxes within the Earthʼs atmosphere.

4) Provide cloud-property estimates which are consistent with the radiative fluxes from the surface 

to the top of the atmosphere.

To accomplish these goals, the CERES data products 

are divided into three major categories, as shown in 

the figure on page 34: ERBE-like Products (top row), 

CERES Surface/TOA Products (middle row), and 

CERES Surface/TOA/Atmosphere (bottom row).

 The ERBE-like products address the first objective 

of long-term continuity of the ERBE TOA fluxes. The 

Surface/TOA Products address the second objective 

and attempt to provide the most direct tie between 

surface radiative-flux estimates and TOA flux measurements. The Surface/TOA/Atmosphere products 

focus on the last two objectives and attempt to derive 

an internally consistent set of atmosphere, cloud, and 

surface-to-TOA radiative fluxes, all within the context of a state-of-the-art radiative-transfer model. In 

this last case, the observed TOA fluxes are used as a 

direct constraint on the model calculations in order 

to implicitly account for non-plane-parallel and other 

poorly modeled atmospheric radiative effects. As in 

ERBE, CERES radiative fluxes will be separately determined for both clear- and cloudy-sky conditions.

 One of the major advances of CERES over ERBE is 

the availability of high spatial and spectral resolution 

cloud imagers for cloud masking, cloud height, and 

cloud-optical-property determination (i.e., VIRS on 

TRMM, MODIS on Terra and Aqua; for more information about Aqua, see EOS Data Products Handbook, Volume 2.) A second major advance is the use 

of one CERES scanner in a cross-track mode (global 

spatial coverage) and a second scanner in a rotatingazimuth-plane mode (complete angular sampling of 

viewing zenith and azimuth). The rotating-azimuthplane scanner data will be combined with nearly simultaneous cloud-imager data to develop a new set 

of improved empirical models of the shortwave (SW) 

and longwave (LW) anisotropy of radiation as a function of surface and cloud type. While it is estimated to 

take two years of these data to develop new angular 

models, they are expected to reduce instantaneous 

TOA flux errors by a factor of three from the ERBE 

levels.

 A detailed listing of the CERES data products and 

their individual parameters can be found in the CERES Data Products Catalog (asd-www.larc.nasa.gov/

DPC/DPC.html). Documentation of the Release 

1 CERES analysis algorithms can be found in the 

CERES Algorithm Theoretical Basis Documents 

(ATBDs) Volumes 0 through 12, where each volume 

covers one of the CERES Data Products and its associated technical algorithms (eospso.gsfc.nasa.gov/

atbd/cerestables.html). A summary of the CERES experiment can be found in Wielicki et al., Bulletin of 

the American Meteorological Society, May, 1996.

 The two CERES instruments on the EOS Terra 

spacecraft have performed nominally since launch 

in December of 1999, with no in-flight anomalies 

to date. The single CERES instrument on the EOS 

TRMM spacecraft, launched in November of 1997, 

collected 9 complete months (January-August 1998, 

March 2000) of science data before a failing voltage 

converter resulted in the loss of science data transmission. All CERES radiometric performance goals 

have been met or exceeded on both platforms. Analysis of the collected radiometric science measurements has indicated minor drifts (~1%/yr) in two of 

the six radiometric channels on the Terra spacecraft. 

These drifts, which occur in the shortwave spectral 

regions (0.2-3.0 μm) of the broadband total channels 

(0.2 - >100 μm) on each of the CERES/Terra instruments, may cause errors in the CERES/Terra Daytime 

longwave data. The longwave portion of the CERES/

Terra total channels (3.0 - >100 μm) are stable at the 

0.25%/yr level while the shortwave and atmospheric 

window channels are stable at better than 0.10%/yr. 
Instrument Descriptions and Data Processing Overviews • TRMM and Terra / CERES Data Products Handbook - Volume 1 33

All channels on the CERES/TRMM instrument were 

stable over the 8 months of science data collection. 

A thorough radiometric validation protocol has been 

developed to continuously monitor the radiometric 

performance of the instruments. This protocol is able 

to characterize and quantify radiometric drifts, or instrument artifacts, so that they may be removed from 

the Level 1 and Level 1b data products. Data users 

are warned that these artifacts are not removed from 

the Terra Edition1 BDS, ES-8, ES-4, and ES-9 data 

sets. All other validated CERES data sets have these 

artifacts removed, including the Terra Edition2 BDS, 

ES-8, ES-4, and ES-9 data sets, as well as all validated TRMM data sets. A more detailed description 

of these artifacts and their impact on the data products 

are documented in the CERES Data Product Quality Summaries and may be found at the web pages 

listed below. These Quality Summaries are dynamic 

and will be updated to document future artifacts and 

quantify their impact on the data products. 

Suggested Reading

Priestley, K. J. et al., 2002.

Lee, R. B. et al., 1998.

asd-www.larc.nasa.gov/ceres

asd-www.larc.nasa.gov/Instrument/intro.html

eosweb.larc.nasa.gov/PRODOCS/ceres/table_

ceres.html

CERES

Key CERES Facts

CERES Home Page:

asd-www.larc.nasa.gov/ceres/docs.html

Launched on TRMM November 27, 1997; on 

Terra December 18, 1999; and on Aqua, May 

4, 2002

Heritage: ERBE

Broadband, scanning radiometer capable of 

operating in cross-track mode, or rotating plane 

mode (bi-axial scanning)

Dual scanners on Terra

Measures Earth’s radiation budget and atmospheric radiation from the top of the atmosphere to the surface

First instrument (cross-track scanning) will 

essentially continue the ERBE mission; the 

second (biaxially scanning) will provide angular 

radiance information that will improve the accuracy of angular models used to derive the 

Earth’s radiative balance

TRMM & Terra Missions
34 Data Products Handbook - Volume 1 • Instrument Descriptions and Data Processing Overviews 

CERES Data Processing Architecture and Products

BDS

Level 1

Geolocated 

Radiance

ES-8

Level 2

ERBE-like Instantaneous

TOA Estimates

ES-4. ES-9

Level 3

ERBE-like Monthly

Averages

SSF

Level 2

Single Scanner TOA/

Surface Fluxes and

Clouds

CRS

Level 2

Clouds and 

Radiative Swath

FSW

Level 3

Monthly Gridded

Radiative Fluxes

and Clouds

SYN

Level 3

Synoptic Radiative

Fluxes and Clouds

AVG, ZAVG

Level 3

Monthly Regional, Zonal,

and Global Radiative

Fluxes and Clouds

SFC

Level 3

Monthly Gridded 

TOA/Surface Fluxes

and Clouds

SRBAVG

Level 3

Monthly TOA/

Surface Averages

ERBE-Like Surface/TOA Surface/TOA/ Atmospher

e

TRMM and Terra / CERES

TRMM & Terra Missions
Instrument Descriptions and Data Processing Overviews • Terra / MISR Data Products Handbook - Volume 1 35

Terra Mission

MISR

Multi-angle Imaging SpectroRadiometer

MISR routinely provides multiple-angle continuous 

coverage of the sunlit Earth with moderately high 

spatial resolution. The instrument obtains multidirectional observations of each scene within seven 

minutes, under virtually the same atmospheric conditions. MISR uses nine individual charge-coupled 

device (CCD)-based pushbroom cameras to observe 

the Earth at nine discrete view angles: one at nadir, 

plus eight other symmetrical views at 26.1°, 45.6°, 

60.0° and 70.5° forward and aftward of nadir. Images at each angle are obtained in four spectral bands 

centered at 446, 558, 672, and 866 nm. Each of the 

36 instrument data channels (four spectral bands in 

each of nine cameras) is capable of providing ground 

sampling of 275 m, 550 m, or 1.1 km. The actual 

observing configuration is selected by commands to 

the instrument.

 The swath width of the MISR imaging data is 

360 km, providing global multi-angle coverage of 

the entire Earth in nine days at the equator, and two 

days at the poles. 

 The instrument design and calibration strategies 

maintain absolute radiometric uncertainty to ±3% 

over bright surfaces and ±6% over dark surfaces, 

with smaller uncertainties in relative band-to-band 

and angle-to-angle radiances. These objectives are 

met by deploying the instrumentʼs On-Board Calibrator (OBC) on a bimonthly basis; the calibrator 

consists of solar diffuser panels and an array of 

radiation-resistant and high-quantum-efficiency diodes. Semiannual field calibration exercises provide 

ground-truth verification of diode performance.

 MISR images are acquired in two observing modes: 

global and local. Global mode provides continuous 

planet-wide observations, with 24 channels operating at 1.1-km resolution and 12 selected channels 

operating at 275 m. Local mode provides data at 

the highest resolution in all spectral bands and all 

cameras for selected 360 km (cross-track) × 300 km 

(along-track) regions. In addition to radiometrically 

calibrated and geo-rectified image products, global 

mode data are used to generate two standard Level 2 

science products during ground data processing: the 

Top-of-Atmosphere (TOA)/Cloud Product and the 

Aerosol/Surface Product. 

 The TOA/Cloud Product provides global information about cloud types (classified by heterogeneity 

and altitude) and their effects on the solar radiance 

and irradiance reflected to space. These are key factors that determine cloud effects on Earthʼs climate. 

This product also provides stereoscopically-derived 

cloud-top heights and cloud-tracked winds, as well 

as a cloud screen for MISR aerosol and surface retrievals.

 The Aerosol/Surface Product contains global aerosol parameters describing the magnitude and natural 

variability in space and time of sunlight absorption 

and scattering by aerosols in the Earthʼs atmosphere. 

These parameters help determine aerosol effects on 

climate and improve our knowledge of the sources, 

sinks, and global budgets of natural and anthropogenic aerosols. They also provide atmospheric correction inputs for surface imaging data acquired by 

MISR and other instruments. The surface parameters 

within the Aerosol/Surface Product characterize land 

surface radiative properties, particularly bidirectional 

and hemispherical reflectances, on a global basis. In 

conjunction with MODIS, they provide improved 

measures of land surface classification, dynamics, 

canopy photosynthesis, and transpiration rates over 

vegetated terrain. They also supplement MODIS 

studies of the biogeochemical cycle in the tropics 

by providing atmospherically-corrected ocean color 

data.

 This handbook describes three ancillary products 

(MIS10, MIS11, and MIS12) that are produced at the 

MISR Science Computing Facility (SCF) and used in 

the generation of other MISR products. These products are available from the Langley Atmospheric 

Sciences Data Center as aids in interpreting MISR 

data.

 Overall, the MISR instrument performance is 

excellent. However, there are several factors affecting science data quality that users should take into 

consideration:

1) Uncertainties in low-light-level radiometry. The 

MISR radiometric scale has been validated to 4% 

uncertainty through comparisons with AirMISR, 

AVIRIS, MODIS, and vicarious calibrations over 

medium to bright land targets. For uniform dark 

targets (equivalent reflectances ~0.05), uncertainties 

are estimated to be about 10%. For very dark targets 

(equivalent reflectances <0.05), especially those embedded in heterogeneous scenes, percentage uncertainties are larger due to the challenge of identifying 

robust calibration standards and the effects of errors 

in system linearity and video offset determination, 

imperfect point-spread function deconvolution, and 

optical ghosting. Specific studies are planned to 

quantify and reduce these uncertainties. 

2) South Atlantic Anomaly effects on radiometry. 

Proton impingement on the MISR focal plane 

CCDʼs can induce random bright points in the imagery. Converting these bright values to equivalent 

reflectance, 0.00007% of pixels outside of the SAA 
36 Data Products Handbook - Volume 1 • Instrument Descriptions and Data Processing Overviews 

Terra Mission

Terra / MISR

have equivalent reflectance errors associated with 

such hits >0.004; during transit through the SAA the 

frequency of pixels increases to 0.06%. These rare 

events do not correlate from one band or angle to 

another, and can be ignored for most applications. 

Certain high level processing algorithms such as 

aerosol retrievals contain data filters that safeguard 

against the use of affected pixels.

3) “Bit flips” and instrument “out-of-synchronization” events. Random occurrences of bit flips 

in packet headers, or instrument out-of-sync events 

which occur when the data throughput exceeds the 

flight computerʼs ability to process packet headers 

properly, can cause momentary data gaps a few lines 

in duration. Most affected image lines are flagged in 

ground data processing, and appear as dark stripes 

across Level 1 imagery. Typically about 0.02% of 

image lines are affected, and usually only in those 

orbits that contain Local Mode sequences.

4) 70° aft camera pointing anomalies. For eight of 

the nine MISR cameras, camera-to-camera co-registration uncertainty is less than 1 pixel (275 m). Timevarying co-registration errors in the 70° aft camera 

have been observed, with errors typically less than 

two pixels, though larger on occasion. Image matching algorithms using “reference orbit imagery” have 

been implemented and usually successfully correct 

these errors. However, they may be present in portions of some orbits, and a geometric data quality 

indicator is being implemented within the Level 1 

georectified radiance product to alert users.

Suggested Reading

Diner, D. J. et al., 2002.

Bruegge, C. J. et al., 2002.

Jovanovic, V. M. et al., 2002.

Bothwell, G. W. et al., 2002. 

Key MISR Facts

MISR Home Page: www-misr.jpl.nasa.gov

Launched on Terra December 18, 1999

Heritage: Galileo, Wide-Field/Planetary 

Camera

Provides radiometrically and geometrically 

calibrated multi-angle radiance measurements of the atmosphere, clouds, and surface with moderately high resolution

Provides global aerosol properties, cloud-top 

heights and cloud-tracked winds, top-ofatmosphere and land-surface albedo, and 

vegetation properties

MISR

The new Diamond Valley Lake Reservoir near the city of Hemet in Riverside County is billed as the largest earthworks 

construction project in U.S. history. Construction began in 1995 and involved 31 million cubic meters of foundation 

excavation and 84 million cubic meters of embankment construction. The above set of MISR images captures a phase 

in the reservoirʼs activation. At the upper left is a view acquired by the instrumentʼs vertical-viewing (nadir) camera on 

March 14, 2000, shortly after the Metropolitan Water District began filling the reservoir with water from the Colorado 

River and Northern California. Water appears darker than the surrounding land. The image at the upper right was 

acquired nearly one year later on March 1, 2001, and shows a clear increase in the reservoirʼs water content. When 

full, the lake will hold nearly a trillion liters of water. On the internet, visit visibleearth.nasa.gov to view a variety of 

MISR data images.
Instrument Descriptions and Data Processing Overviews • Data Products Handbook - Volume 1 37

MODIS

 Moderate Resolution Imaging Spectroradiometer

MODIS is an EOS facility instrument designed 

to measure biological and physical processes on a 

global basis every one to two days. Currently flying 

onboard the Terra and Aqua satellites, the instrument 

will provide long-term observations from which to 

derive an enhanced knowledge of global dynamics 

and processes occurring on the surface of the Earth 

and in the lower atmosphere. This multidisciplinary 

instrument will yield simultaneous, congruent observations of high-priority atmospheric (cloud cover and 

associated properties), oceanic (sea-surface temperature and chlorophyll), and land-surface features 

(land-cover changes, land-surface temperature, and 

vegetation properties). The instrument is continuing 

to make major contributions to the understanding of 

the global Earth system, including interactions between land, ocean, and atmospheric processes.

 The MODIS instrument employs a conventional 

imaging-radiometer concept, consisting of a crosstrack scan mirror and collecting optics, and a set of 

linear detector arrays with spectral interference filters 

located in four focal planes. The optical arrangement 

provides imagery in 36 discrete spectral bands from 

0.4 to 14.5 μm, selected for diagnostic significance in 

Earth science. The spectral bands have spatial resolutions of 250 m, 500 m, or 1 km at nadir; signal-tonoise ratios of greater than 500 at 1-km resolution (at 

a solar zenith angle of 70°); and absolute irradiance 

accuracies of ±5% from 0.4 to 3 μm (2% relative to 

the Sun) and 1% or better in the thermal infrared (3 

to 14.5 μm). Each of the MODIS instruments, operating continuously, provides daylight reflection and 

day/night emission spectral imaging of any point on 

the Earth at least once every two days.

 Many MODIS products are made over time intervals ranging from about one week to a month or 

a season. These products are better suited for use by 

investigators interested in seasonal phenomena. In 

some cases these products are made to reduce data 

volume; in others they are made to provide cloud-free 

coverage within a defined time period. Because the 

ground track of the EOS spacecraft follows a 16-

day repeat cycle, and because there may be biases in 

the multiday data sets, based on viewing geometry 

at specific locations, the MODIS “week” has been 

defined as an 8-day period. The land and ocean data 

products are based upon 8-day weeks beginning on 

January 1 of each year. The atmosphere products are 

also based on 8-day weeks, but these begin with the 

first 8-day period of the Aqua MODIS sensor that is 

concurrent with the Terra MODIS schedule and run 

in an unbroken sequence thereafter. Thus, the land 

and ocean weeks will not, in general, coincide with 

the atmosphere weeks.

 MODIS will provide specific global data products, 

which include the following:

• Surface temperature with 1-km resolution, day and 

night, with absolute accuracy of 0.2 K for oceans 

and 1 K for land;

• Ocean color, defined as ocean-leaving spectral radiance within 5% from 415 to 653 nm, enabled by 

atmospheric correction from near-infrared sensor 

channels;

• Chlorophyll fluorescence within 50% at surfacewater concentrations of 0.5 mg m-3;

• Concentrations of chlorophyll a within 35%;

• Daily and annual ocean primary productivity.

• Vegetation/land-surface cover, conditions, and 

productivity

– Net primary productivity, leaf-area index, and 

intercepted photosynthetically active radiation

– Land cover type, with change detection and 

identification

– Vegetation indices with improved sensitivity to 

the global range of biomes;

• Snow cover and sea ice cover;

• Land-surface reflectance (atmospherically corrected) with bidirectional reflectance and albedo;

• Cloud cover with 250-m resolution by day and 

1,000-m resolution at night;

• Cloud properties characterized by cloud-droplet 

phase, optical thickness, droplet size, cloud-top 

pressure, and emissivity;

• Aerosol properties defined as optical thickness, 

particle size, and mass transport;

• Fire occurrence, size, and temperature;

• Global distribution of precipitable water; and

• Cirrus-cloud cover.

 

Terra / MODIS

Terra Mission
38 Data Products Handbook - Volume 1 • Instrument Descriptions and Data Processing Overviews 

Terra Mission

Terra / MODIS

The data-product descriptions in the main part of 

this handbook refer to MODIS spectral bands by 

number. These are all defined in the table on the 

following page.

 Since the launch of the MODIS instrument on the 

EOS Terra spacecraft in December 1999 the overall 

instrument operation has been very good. However, 

analyses of the data from the instrument starting immediately after the first observations were received on 

February 24, 2000 revealed several factors in the data 

that needed improvement in order to reach the full 

potential inherent in MODIS observations. Several 

of the factors have been manifested in images or the 

MODIS data as striping or noise. Examples of the several specific factors that have been examined include 

optical cross-talk from band 31 into bands 32-26 and 

shortwave infrared (SWIR) thermal leaks, electronic 

cross-talk among bands 5-7 and 20-26, non-uniform 

channel to channel response within a band, sensor 

time-dependent, gain changes, mirror-rotation correlated noise, etc. These effects have largely been 

analyzed and corrections instituted into the MODIS 

Level 1 and related data products so that the specified 

uncertainly levels in radiance and reflectances have 

been achieved and the products now available from 

the data archives are validated for scientific studies. 

References providing more extended descriptions of 

these factors and attendant corrections and related 

instrument performance are listed below. MonitorMODIS

ing of the instrument performance continues so as 

to make adjustments for time-dependent behavior 

or other changes. Where significant changes occur, 

adjustments in the relevant algorithms will be made 

and included as appropriate in processing and reprocessing of the MODIS data set.

Suggested Reading

Guenther, B. et al., 2002.

Barnes, W. L. et al., 1998.

Esaias, W. E. et al., 1998.

Guenther, B. et al., 1998.

Wolfe, R. E. et al., 2002.

www.mcst.ssai.biz/mcstweb/performance/terra_

instrument.html

www.mcst.ssai.biz/mcstweb/L1B/product.html 

www.mcst.ssai.biz/mcstweb/index.html

Key MODIS Facts

MODIS Home Page: modis.gsfc.nasa.gov

Launched on Terra December 18, 1999, and 

on Aqua May 4, 2002

Heritage: AVHRR, HIRS, Landsat TM, and 

Nimbus 7 CZCS

Medium-resolution, multi-spectral, cross-trackscanning radiometer

Measures biological and physical processes

Typhoon Yutu seems to span the entire South China Sea from the 

Philippines (lower right) to Taiwan (upper right) and eastward to 

the Chinese mainland in this MODIS image from July 24, 2001.

On the internet, visit visibleearth.nasa.gov to view a variety of 

MODIS data images in color.
Instrument Descriptions and Data Processing Overviews • Data Products Handbook - Volume 1 39

 Center Pixel

Primary Use Band Wavelength Bandwidth Size Ltyp SNR @ Ltyp

 (nm) (nm) (m) 

Land/Cloud/Aerosol 1 645 48 250 21.8 129

Boundaries 2 857 38 250 24.7 201

Land/Cloud/Aerosol 3 466 19 500 35.3 243

Properties 4 554 20 500 29.0 228

 5 1242 24 500 5.4 74

 6 1629 29 500 7.3 270

 7 2114 56 500 1.0 111

Ocean Color/ 8 411 15 1000 44.9 880

Phytoplankton/ 9 442 10 1000 41.9 838

Biogeochemistry 10 487 11 1000 32.1 803

 11 530 12 1000 27.9 754

 12 547 10 1000 21.0 750

 13 666 10 1000 9.5 914

 14 677 11 1000 8.7 1088

 15 746 10 1000 10.2 600

 16 866 16 1000 6.2 517

Atmospheric 17 904 35 1000 10.0 167

Water Vapor 18 936 14 1000 3.6 57

 19 935 46 1000 15.0 250

Cirrus Clouds 26 1383 35 1000 6.0 150

 

 Center Pixel SNR @ Ltyp =

Primary Use Band Wavelength Bandwidth Size Ltyp(Ttyp) Ltyp/NEΔL

 (μm) (μm) (m) 

Surface/Cloud 20 3.79 .19 1000 0.45(300K) 900

Temperature 21 3.99 .08 1000 2.38(335K) 203

 22 3.97 .09 1000 0.67(300K) 838

 23 4.06 .09 1000 0.79(300K) 988

Atmospheric 24 4.47 .09 1000 0.17(250K) 142

Temperature 25 4.55 .09 1000 0.59(275K) 454

Water Vapor 27 6.75 .25 1000 1.16(240K) 252

 28 7.33 .33 1000 2.18(250K) 641

Cloud Properties 29 8.52 .35 1000 9.58(300K) 2661

Ozone 30 9.74 .30 1000 3.69(250K) 445

Surface/Cloud 31 11.02 .54 1000 9.55(300K) 2809

Temperature 32 12.03 .52 1000 8.94(300K) 1825

Cloud Top 33 13.36 .31 1000 4.52(260K) 452

Altitude 34 13.68 .33 1000 3.76(250K) 298

 35 13.91 .33 1000 3.11(240K) 221

 36 14.19 .29 1000 2.08(220K) 107

SNR = Signal-to-Noise Ratio

NE = Noise Equivalent 

Ltyp (in W/m2-μm-sr) = Spectral radiance at typical conditions for 

this product

Ttyp = Temperature at typical conditions for this product

MODIS Spectral Band Properties and Applications

Terra / MOTerra Mission
40 Data Products Handbook - Volume 1 • Instrument Descriptions and Data Processing Overviews 

Terra Mission

Terra / MODIS

MODIS Data Processing Architecture and Products, general Level 1

MODIS Processing

 MOD02

Calibrated L1B Radiances

Level 0 Level 2/Level 2 Gridded (L2G)

MOD35

Cloud Mask

 MOD02 MOD03

MOD02, MOD03, MOD07, MOD35, Grid Pointer, Grid Angular

 Level 1, Level 2 Atmosphere, Level 2 Ocean, Grid Angular, Grid Pointer

Ocean Color 

(see Oceans Chart)

Ocean Color

(L3 prior 8-day period)

MOD28

Sea Surface

Temperature 

(L3 prior 8-day period)

Grid Angular

L2G Land

Angular Data 

L2, L2G, L3, Legend:

MOD04

Aerosol Product

MOD02, MOD03, MOD04, MOD07, MOD35, Grid Pointer, Grid Angular

Oceans

Land

Atmos

MOD05

Precipitable Water

MOD28

Sea Surface Temperature 

Instrument Packet Data

MOD01

Level 1A

MOD03

Geolocation Data Set

MOD07

• Total Ozone Burden

• Stability Indices

• Temperature and

 Moisture Profiles 

• Precipitable Water (IR)

Grid Pointer

L2G Land

Pointer Files 

MOD06

Cloud Product
Instrument Descriptions and Data Processing Overviews • Data Products Handbook - Volume 1 41

MODIS Data Processing Architecture and Products, general (continued)

Terra / 

MOD28

Sea Surface

Temperature

(1-, 8-day, 

monthly, yearly)

MOD27

Ocean Productivity

(8-day, yearly)

Ocean Color

(1-, 8-day,

monthly, yearly)

MODIS Processing

(Continued)

Level 2 Gridded (L2G)

MOD08

L3 Atmosphere

Products

(1-, 8-day, monthly)

Level 3 Level 4

MOD12

Land Cover

Type

(96-day)

MOD12

Land Cover Type

(L3 prior period or

at-launch)

MOD11

(L3 prior period)

MOD10

Snow Cover

MOD11

Land Surface

Temp. & Emis. 

MOD09

Surface

Reflectance 

MOD29

Sea Ice Cover

Level 2 Atmosphere, Level 2 Ocean, Level 2G Land

MOD17

Vegetation Production (8-day, yearly)

MOD16

Evapotranspiration (8-day, yearly) Note: This is an Aqua product only

MOD29

Sea Ice Cover

(1-, 8-day)

MOD43

(L3 prior period)

 Level 1, Level 2 Atmosphere, Level 2 Ocean , L2G Grid Pointer, L2G Grid Angular

MOD12

(L3 prior period or

at-launch)

MOD44

(L4)

MOD44A

Vegetative Cover

Conversion

(3-month. yearly)

MOD40

Burn Scars

(8-, 16-day, 

monthly)

L2, L2G, L3, Legend:

Oceans

Land

Atmos

BRDF/Albedo

Land Cover Type

Vegetation Cover

Conversion

MOD14

Thermal

Anomalies

MOD43

BRDF/Albedo

(16-day)

MOD13

Vegetation

Indices (16-day,

monthly)

MOD11

Land Surface

Temp. & Emis.

(8-day, monthly)

MOD10

Snow Cover

(1-, 8-day)

MOD14

Thermal Anomalies

(1-, 8-day, monthly)

MOD15

LAI and FPAR

(1-, 8-day)

Terra Mission
42 Data Products Handbook - Volume 1 • Instrument Descriptions and Data Processing Overviews 

Terra Mission

Terra / MODIS

MODIS Data Processing Architecture and Products, land Level 1 Level 0

 MOD02 MOD03

Level 

2

MODIS

Atmosphere Processing

Level 3

MOD03, Level 2 Atmosphere Products

MOD02, MOD03, MOD07, MOD35

MOD08

L3 Atmosphere Products

• Aerosol • Water Vapor

• Clouds • Stability Indices

• Ozone

(1-, 8-day, monthly)

Instrument Packet Data

MOD01

Level 1A

 MOD02

Calibrated L1 Radiances

MOD03

Geolocation Data Set

MOD35

Cloud Mask

MOD07

Atmospheric Profiles

• Total Ozone Burden

• Stability Indices

• Temperature and

 Moisture Profiles

• Precipitable Water (IR)

MOD06

Cloud Product

• Cloud Top Properties

 (temperature, height,

 emissivity) 

• Cloud Particle Phase

• Cloud Fraction

• Cloud Optical Properties

 (optical thickness and

 particle size) 

MOD05

Precipitable

Water Product

• Solar Algorithm

• IR Algorithm

MOD04 

Aerosol Product

• Aerosol Optical

 Thickness 

• Aerosol Size

 Distribution 
Instrument Descriptions and Data Processing Overviews • Data Products Handbook - Volume 1 43

MODIS Data Processing Architecture and Products, atmosphere

Terra / MODIS

MODIS 

Land Processing

 MOD29

Sea Ice Cover

MOD10

Snow Cover

Level 2 Gridded (L2G) Level 1

 MOD02

Calibrated L1B Radiances

Level 0

MOD02, Grid Pointer, Grid Angular

MOD11

Land Surface

Temp. & Emis.

Level 2G

MOD43

BRDF/Albedo

(16-day)

MOD12

Land Cover Type

(96-day)

MOD17

Vegetation Production

(8-day, yearly)

MOD16

Evapotranspiration (8-day, yearly) Note: This is an Aqua product only

MOD29

Sea Ice Cover

(1-, 8-day)

MOD14

Thermal Anomalies

(1-, 8-day, monthly)

Level 3 Level 4

MOD12

(L3 prior period or

at-launch)

MOD43

BRDF/Albedo

(Prior period) 

MOD44

Vegetative Cover Conversion 

(3-month, yearly)

MOD40

Burn Scars

(8-, 16-day,

monthly)

MOD04

Aerosol

Product

MOD44

Vegetation Cover

Conversion (L4)

Instrument Packet Data

MOD01

Level 1A

Grid Pointer

Product

Grid Angular

Data Product

MOD03

Geolocation Data Set

MOD35

Cloud Mask

MOD07

Atmospheric

Profiles

MOD12 

Land Cover Type

(L3 prior period or

at-launch)

MOD11

(L3 prior period)

MOD43

BRDF/Albedo

(L3 prior period)

MOD09

Surface Reflectance

MOD14

Thermal Anomalies

MOD05

Precipitable

Water 

MOD35

Cloud Mask

MOD04

Aerosol Product

MOD11

Land Surface

Temp. & Emis.

(8-day, monthly)

MOD13

Vegetation Indices

(16-day, monthly)

MOD10

Snow Cover

(1-, 8-day)

MOD15

LAI and FPAR

(1-, 8-day)

Terra Mission
44 Data Products Handbook - Volume 1 • Instrument Descriptions and Data Processing Overviews 

Terra Mission

MODIS Data Processing Architecture and Products, oceans. A complete set of these products will be produced for both Terra and 

Aqua MODIS sensor data. In addition, a merged Terra/Aqua MODIS Ocean product set will be produced at Levels 3 and 4 in the 

post-Aqua launch period.

Terra / MODIS

Level 

2 Level 3 Level 

4

MODIS

Ocean Processing

MOD04

Sea Aerosol 

Product

MOD28

Sea Surface

Temperature

(1-day)

MOD28

Sea Surface

Temperature

(1-, 8-day,

monthly, yearly)

MOD35

Cloud Mask

MOD04

Aerosol

Product 

MOD27

Ocean

Productivity

(8-day, yearly)

Ocean Color:

MOD18 Water-Leaving Rad.

MOD19 Pigment Conc.

MOD20 Chlorophyll Fluor.

MOD21 Chlorophyll Conc.

MOD22 PAR/Interim PAR

MOD23 Suspended Solids Conc.

MOD24 Organic Matter Conc. 

MOD25 Coccolith Conc.

MOD26 Water Attenuation Coef.

MOD31 Phycoerythrin Conc.

MOD36 Absorption Coefficients

MOD37 Aerosol Optical Depth

MOD39 Clear-Water Epsilon 

MOD05

Precip. Water

MOD28

Sea Surface

Temperature

(L3 prior 

8-day period)

Level 

0 Level 1

 MOD02 MOD03

Ocean Color

(L3 prior 

8-day period)

Instrument Packet Data

MOD01

Level 1A

MOD03

Geolocation Data Set

 MOD02

Calibrated L1B Radiances

Ocean Color

(1-, 8-day,

monthly, yearly)
Instrument Descriptions and Data Processing Overviews • Terra / MOPITT Data Products Handbook - Volume 1 45

MOPITT

Measurements of Pollution in The Troposphere

The MOPITT experiment is provided under a Memorandum of Understanding with the Canadian Space 

Agency (CSA). MOPITT measures thermal emission 

in the 4.6-μm band of carbon monoxide (CO) and 

reflected solar radiation in the 2.3-μm band of CO 

and 2.2-μm band of methane (CH4). Tropospheric CO 

profile and total columns of CO and CH4 are derived 

from those measurements in the retrieval process.

 CO is one of the most important trace species in 

tropospheric chemistry. The full range of the effects of the increased/decreased concentration of 

CO is not fully understood at the present time, but 

it is known that CO is photochemically active and 

plays a major part in the concentration of hydroxyl 

(OH) radicals in the troposphere. Increased CO may 

deplete tropospheric OH radicals, thereby reducing 

the yearly removal of many natural and anthropogenic trace species. In particular, this effect may add 

to the increase of CH4, which in turn could further 

reduce OH concentration. Increased CO may also 

indirectly intensify global warming and perturb the 

stratospheric ozone layer by increasing the lifetime 

of trace gases such as CH4, methyl chloride (CH3Cl), 

and chlorofluorocarbons (CFCs). CH4 is also a very 

effective greenhouse gas, and the change of its 

concentration due to human activities will impact 

global climate change. Measurements of CO and 

CH4 will enhance our knowledge of the chemistry 

of the troposphere, and particularly how it interacts 

with the surface/ocean/biomass systems, atmospheric 

transports, and the carbon cycle.

 MOPITT operates on the principle of correlation spectroscopy, a non-dispersive spectroscopy 

technique. It uses the target gas itself contained in 

a gas cell as the filter to achieve high spectral resolution as well as high signal to noise. Both pressure 

modulation cells (PMC) and length modulation cells 

(LMC) are used in MOPITT. By cycling the amount 

of gas in the absorption cell between two states (low 

and high pressure in the case of the PMC, and long 

and short path in the case of the LMC), the detector 

will be alternately looking through two different filters that exactly correlate with the target gas spectra. 

The difference of the two signals will be identical to 

the output of a system in which the gas cell and its 

modulator are replaced by an optical filter with nonzero transmission only at the spectral line positions of 

the target gas (CO and CH4 for MOPITT). Therefore, 

the difference signal is most sensitive to the contributions by the target gas and is not sensitive to the 

contributions of other gases. The average of the two 

signals can also be obtained. It has the property that 

its transmittance is near unity away from the lines 

in the cell, but it reduces the signals at the centers 

of the lines. Thus, it is sensitive to other gases, and 

especially to the surface contribution to the upwelling 

radiation in the spectral regions of interest.

 MOPITT is a cross-track scanning instrument 

which collects data with a scan line of four adjacent 

22-km square pixels at nadir and at 14 “stare” positions on either side of nadir. The pixels are arranged 

in the along-track direction. The stare positions are 

interleaved so as to produce nearly contiguous coverage in the cross-track and along-track directions. 

For each orbit, a swath ~640 km wide is sampled. 

This allows complete sampling of the Earthʼs surface 

about every three days.

 MOPITT Level 2 products include: 1) tropospheric 

CO profiles with a resolution of 22 km horizontally, 4 

km vertically and with an accuracy of 10% throughout 

the troposphere; 2) total column of CO with a 10% 

accuracy and 22-km horizontal resolution; and 3) total 

column of CH4 with a precision of 1% and horizontal 

resolution of 22 km. The column amounts of CO and 

CH4 will only be available on the sunlit side of the 

orbit. Level 3 products to be developed postlaunch 

will include gridded three-dimensional global maps 

of CO and global maps of CH4 total column.

 The MOPITT instrument began operations on 

March 2, 2000 and continued in full operation until 

May 7, 2001 when a cooler issue caused one half of 

the instrument to be shut down. From August 23, 2001 

to the present time, the other side of the instrument 

has continued to produce satisfactory data and recent 

analysis shows that the planned redundancy permits 

almost complete restoration of the data quality. During the first few months of instrument operation 

several artifacts in the data stream were detected 

and dealt with, including issues of filter changes 

with temperature and data issues with the pressure 

modulator channels. These have been resolved, and 

the carbon monoxide data for the 4.7 μm band are 

being routinely processed. Carbon monoxide data up 

to May 7, 2001 has been validated. Work continues on 

the shortwave channels which have proved to have a 

number of issues, both instrumental and geophysical, 

which have delayed release of the data products. Instrument performance analysis shows that there has 

been virtually no degradation of optics, filters, or detectors since launch. Periodic removal of the water ice 

which accumulates on the cold filter assemblies has 

been required less and less often and is now projected 

to be performed about once per year.

Terra Mission
46 Data Products Handbook - Volume 1 Terra / MOPITT • Instrument Descriptions and Data Processing Overviews

Terra Mission

This is a global image of carbon monoxide averaged over the month of January 2001 at an altitude of about 

3 km (700 hPa). The high concentrations of CO in the northern regions of the United States are mainly due 

to industrial processes and urban emissions, and over western central Africa they are due to widespread 

biomass burning. Areas of missing data show the presence of persistent cloud which inhibit retrievals. 

On the internet, visit visibleearth.nasa.gov and www.eos.ucar.edu/mopitt/dataimages/index.html to view a 

variety of MOPITT images.

Suggested Reading

Edwards, D. P. et al., 2002.

Key MOPITT Facts

MOPITT Home Page:

www.atmosp.physics.utoronto.ca/

MOPITT/home.html

www.eos.ucar.edu/mopitt

Launched on Terra December 18, 1999

Heritage: Pressure-modulated cell elements 

used in the PMR, SAMS, and ISAMS instruments, using similar correlation spectroscopy 

techniques

Eight-channel correlation radiometer with 

cross-track scanning

Instrument provided by the Canadian Space 

Agency

MOPITT

-150 -120 -90 -60 -30 0 30 60 90 120 150

60

30

0

-30

-60

0 50 60 70 80 90 100 120 140 160 200 ppbv
47

Chapter 4

Radiance and

Imagery Products

TRMM

CERES

LIS

PR

TMI

VIRS

Terra

ASTER

CERES

MISR

MODIS

MOPITT
48 Data Products Handbook - Volume 1 Overview • Radiance and Imagery Products 

Radiance and Imagery Products – An Overview

of electromagnetic radiation already present. The last 

technique uses active sensors that generate their own 

radiation and monitor the return signals from echoes 

off the surface and atmosphere. TRMM uses all four 

techniques. The Terra platform concentrates on the 

first two using high spectral and spatial resolutions.

 On the Terra satellite, radiances are derived using MODIS, ASTER, MOPITT, CERES, and MISR 

sensors. The reflected solar and thermal spectrum is 

very sensitive to changes in water vapor, ozone, and 

aerosol concentrations, to surface reflectivity, emissivity, and temperature, and to cloud properties such 

as droplet size.

 TRMM has five sensors: 1) the Precipitation Radar 

(PR), 2) the TRMM Microwave Imager (TMI), 3) 

the Visible Infrared Scanner (VIRS), 4) CERES, and 

5) LIS. Information on tropical precipitation, the radiation budget, and the Earthʼs lightning climatology 

is an important goal for this satellite. 

Imagery Data: In the List of Products, there are a 

number of geophysical parameters that can be classified as imagery products, which are used as input in 

the derivation of higher-level geophysical data products. These imagery products include masking-type 

data such as cloud cover and scene type. They include 

intermediate-type products such as water-leaving radiances used in the derivation of oceanic biological 

activity and photosynthetically active radiation (PAR) 

used in biological studies, and they include geolocation data allowing comparisons between instruments 

of the same location or across time.

Input Data to Other Instruments: Radiance and 

imagery data from one instrument are often used as 

input for processing data from another instrument. For 

example, the ASTER calibration uses atmospheric 

absorption and scattering properties derived from 

simultaneous MISR and MODIS observations. In 

part then, the calibration of the ASTER radiances 

may have a dependence on MISR and MODIS radiances.

Product Interdependency and 

Continuity with Heritage Data

The radiance values are the first physical quantity 

derived from the raw instrument counts. The accuracy 

and self-consistency of the higher-level geophysical 

products are dependent on the accuracy of the radiances and, hence, upon the calibration of the sensors. 

Relationship to Global Change Issues

The instruments on TRMM and Terra detect electromagnetic radiation in the visible, infrared, and 

microwave wavelengths. Each type of wavelength 

data provides different information about the Earthatmosphere system. Because there are dozens of 

geophysical parameters such as atmospheric composition, surface properties, and biological attributes, 

many independent wavelength measurements are required to monitor the Earth system. Radiances may 

be thought of as the intensity or brightness of the 

radiation. Accurate radiances are a fundamental and 

essential requirement for deriving correct values for 

all the higher-level geophysical parameters monitored 

by the Earth Science Enterprise (ESE). To ensure 

that correct geophysical measurements are made, it 

is crucial to have well-calibrated radiances within 

spectral bands of individual instruments, across instruments, across platforms, and for time intervals 

from days to decades. 

 In addition to the radiances, there are a number of 

ancillary data products such as cloud cover, surface 

reflectivity and topography, aerosols, and scene type 

that are important input data sets in the derivation 

of other geophysical quantities. These ancillary data 

sets are used for data processing within a single instrument or are transferred from one instrument to 

another as part of their input data streams. For our 

purposes we can call these ancillary geophysical data 

sets “imagery products.” In addition, many of the 

land and ocean radiance products are grouped here. 

In this section, we review some of the radiance and 

imagery data products from nine satellite sensors on 

two satellite platforms and explain how the products 

support the overall global change program.

Product Overview

Radiances: Electromagnetic energy detected by 

satellites arises in the Earth-atmosphere system by 

one of four processes: 1) reflection of solar radiation 

off the land and oceans or from the atmosphere, 2) 

thermal infrared to microwave radiation by surface 

and atmosphere, 3) non-thermal emission of radiation, most prominently by lightning discharges, and 

4) return of radiation emitted by the sensor (such as 

a radar). Detecting each of these four types of electromagnetic energy requires a completely different 

instrument design and technique. The first three techniques involve passive sensors which take advantage 
Radiance and Imagery Products • Overview Data Products Handbook - Volume 1 49

Image products from one instrument often influence 

the data processing paths used by other instruments. 

For example, CERES data processing uses MODIS 

scene classification input. ASTER calibration may 

have a dependency upon MODIS calibration. Topographic information such as MISRʼs digital elevation 

model (MIS10) is used in the interpretation of satellite 

radiances. In addition, a rough topography will have 

a different reflectivity in different directions (referred 

to as the bi-directional reflectance distribution function or BRDF) than will a smooth topography. The 

topographic information is a required input for the 

interpretation of MISR and many other types of satellite observations.

 The detection of changes in the Earthʼs environment 

requires accurate and self-consistent observations of 

physical phenomena over many years. The EOS and 

TRMM programs have placed much emphasis on 

making well-calibrated measurements. Calibrating 

the measurements over many years is complicated because many satellites are involved, which may or may 

not have overlapping observations; the instruments 

themselves are changing (because of degradation, for 

example); and there are numerous wavelength bands 

from the visible to the microwave requiring different 

calibration methods.

 Radiation flux and budget observations began 

with the Earth Radiation Budget (ERB) and Earth 

Radiation Budget Experiment (ERBE) and continue 

with the CERES instruments on TRMM and Terra, 

along with the Aqua mission. Calibrated radiances 

from all these instruments are fundamentally important for the documentation of subtle changes in 

the Earthʼs radiation budget. ASTER advances the 

technology of imagers such as Landsat and Franceʼs 

Systeme pour lʼObservation de la Terre (SPOT). MODIS provides improved spectral resolution compared 

to the Advanced Very High Resolution Radiometer 

(AVHRR), the Coastal Zone Color Scanner (CZCS), 

Sea-viewing Wide Field-of-view Sensor (SeaWiFS), 

and earlier scanning spectroradiometers. MOPITT, 

MISR, and the TRMM instruments represent significant advances over their predecessors as well. 

A significant portion of these improvements can be 

directly attributed to the increased emphasis on calibration of all the sensors coming online in the EOS 

era. 

Suggested Reading

Brest, C. L., and S. N. Goward, 1987.

Boccippio, D.J. et al., 2000.

Christian, H. J. et al., 1989.

Gillespie, A. R. et al., 1986.

Goodman, A. H., and A. Henderson-Sellers, 1988.

Gordon, H. R., 1978.

Kummerow, C. et al., 1998.

Kummerow, C. et al., 2000.

Slater, P. N. et al., 1987.
50 Data Products Handbook - Volume 1 • Radiance and Imagery Products 

LIS Background Product 

(LIS01)

Product Description

The Level 1A product consists of LIS background 

images, reported intermittently by the instrument 

as memory buffers allow (lightning event data have 

storage priority over background image data). Typical time between backgrounds is 15-30 sec, although 

during high lightning-rate scenes, backgrounds may 

not be reported at all. The purpose of this product is 

to provide a “snapshot” of the LIS field-of-view when 

the lightning data volume is low. When available, 

backgrounds are also used for calibration of lightning event radiances; between backgrounds, cloud 

brightness (and hence LIS radiance calibration for 

transient events) is approximated by a solar zenith 

angle relationship.

 Backgrounds are organized into orbit granules, 

segmented at the southernmost nadir point of each 

orbit. Data include the same orbit summary and 

one-second Level 1 and 3 data from the primary LIS 

product granules, a background image summary 

record containing the time, nadir point, and corner 

locations of each image, and the actual background 

images themselves, containing 12-bit background 

amplitudes of the 128 × 128 CCD array at the time 

of scene reporting. Navigation transform matrices 

in the one-second data container allow full geolocation of these images, while diagnostic records in 

the one-second data provide status flags for various 

instrument, platform, environmental, and processing 

conditions.

LIS Background Product Summary

Coverage: Global, 35°N to 35°S

Processing Level: 1A

Spatial/Temporal Characteristics: 

4 km × 4 km, orbit granules, intermittent 

reporting

Key Measurements: Background, summary, 

navigation and status data

Product Type: Standard, at-launch

Maximum File Size: 5.54 MB

File Frequency: 1/orbit

Primary Data Format: HDF-EOS

Browse Available: Yes

Additional Product Information: 

thunder.nsstc.nasa.gov/data/

DAAC: Global Hydrology Resource Center, 

ghrc.msfc.nasa.gov

Science Team Contacts: H. Christian, 

S. Goodman

TRMM / LIS
Radiance and Imagery Products • Data Products Handbook - Volume 1 51

PR Radar Total Power, Noise, 

and Reflectivity (PR 1B-21, 

1C-21)

Product Description

The Level 1B Precipitation Radar (1B-21) data set 

contains returned power and noise information, 

along with satellite information such as navigation 

(spacecraft position and velocity) and radar calibration information. The Level 1C PR data (1C-21) are 

radar reflectivity derived from the Level 1B data 

where data are excluded if no significant echoes are 

found (i.e., little or no rain activity) and noisy data 

are suppressed and set to predefined values.

Suggested Reading

Kozu, T. et al., 2001.

PR Radar Total Power, Noise, and 

Reflectivity Summary

Coverage: 36°S to 36°N; 15-16 orbits per day 

with a swath width of 220 km before August 

2001 and 253 km after August 2001 (satellite 

altitude was raised from 350 km to 402.5 km)

Spatial/Temporal Characteristics: 5 km (after 

August 2001)

Key Geophysical Parameters: Rainfall profile

Processing Level: 1B

Frequency: 13.8 GHz

Product Type: Standard, at-launch

Maximum File Size: 160 MB

File Frequency: 1/orbit

Primary Data Format: HDF

Browse Available: Yes

Additional Product Information: 

trmm.gsfc.nasa.gov

tsdis.gsfc.nasa.gov

DAAC: Goddard Space Flight Center Earth 

Sciences DAAC, daac.gsfc.nasa.gov

Science Team Contact: K. Okamoto

TMI Calibrated Brightness

Temperatures (TMI 1B-11)

Product Description

This Level 1B data set contains calibrated brightness 

temperatures, in degrees Kelvin, from the high- and 

low-resolution TMI channels. Pixel geolocation, 

calibration, and instrument pointing and location 

information are also included.

 The TMI is similar to the SSM/I instrument flown 

on the DMSP satellites with two key differences: 1) 

the addition of vertically and horizontally polarized 

10-GHz channels; and 2) the scan geometry is the 

same for every scan instead of alternating between an 

A scan and a B scan. The TMI Level 1B data format 

is similar to the SSM/I Wentz Antenna Temperature 

data; however, some changes were made due to 

instrument characteristics and feedback from the 

investigator community.

 Aside from the rainfall parameters derived as part 

of the TRMM mission, TMI data may also be used 

to derive sea surface wind speed, atmospheric water 

vapor content and cloud liquid water content over 

the ocean. Over land, passive microwave frequencies 

found on TMI can be useful for monitoring soil moisture and vegetation.

TRMM / PR and TMI
52 Data Products Handbook - Volume 1 TRMM / TMI and VIRS • Radiance and Imagery Products 

TMI Calibrated Brightness 

Temperatures Summary

Coverage: 38°S to 38°N; 15-16 orbits per day 

with a swath width of 760 km before August 

2001 and 875 km after August 2001 (satellite 

altitude was raised from 350 km to 402.5 km)

Spatial/Temporal Characteristics: 5 km

Key Geophysical Parameters: Rainfall profile

Processing Level: 1B

Frequency: 10.65, 19.35, 37.0, 85.5 GHz 

(vertical, horizontal); 21.3 GHz (vertical only)

Product Type: Standard, at-launch

Maximum File Size: 15 MB

File Frequency: 1/orbit

Primary Data Format: HDF

Browse Available: Yes

Additional Product Information: 

trmm.gsfc.nasa.gov

tsdis.gsfc.nasa.gov

DAAC: Goddard Space Flight Center Earth 

Sciences DAAC, daac.gsfc.nasa.gov

Science Team Contact: J. Shiue

VIRS Calibrated Radiances 

(VIRS 1B-01)

Product Description

This Level 1B data set contains calibrated radiances in 

mW/(cm2 μm sr) from the five channels of the VIRS 

instrument. Geolocation, data quality, and solar and 

scan geometry information are appended to the five 

channels. Data are collected continuously from all 

5 channels (0.62, 1.61, 3.78, 10.83, and 12.03 μm). 

Radiometric calibration is performed using ground 

testing and in-flight data. In-flight calibration of 

channels 1 and 2 is via an onboard solar diffuser and 

occasional views of the moon. Channels 3-5 use an 

onboard blackbody and a space view to update calibration parameters before each scan line. Processing 

to compensate for undesirable sensor effects is performed, but the data are not geometrically corrected 

or resampled.

 The VIRS channels are similar to those of AVHRR 

and MODIS, and as such the radiance data may be 

used for a variety of atmospheric, terrestrial, and 

oceanographic research. This product is an input 

to TRMM 3B-42, which generates surface rainfall 

derived from geostationary satellites and calibrated 

by the data from the TRMM sensors.

Suggested Reading

Barnes, R. et al., 2000.

Lyu, C. et al., 2000.

Lyu, C., and W. Barnes, 2003.
Radiance and Imagery Products • TRMM / VIRS Data Products Handbook - Volume 1 53

VIRS Calibrated Radiances Summary

Coverage: 38°S to 38°N; 15-16 orbits per day 

with a swath width of 720 km before August 

2001 and 830 km after August 2001 (satellite 

altitude was raised from 350 km to 402.5 km)

Spatial/Temporal Characteristics: 2 km

Key Geophysical Parameters: Rainfall rate

Processing Level: 1B

Wavelengths: 0.62, 1.61, 3.78, 10.83, 

12.03 μm

Product Type: Standard, at-launch

Maximum File Size: 95 MB

File Frequency: 1/orbit

Primary Data Format: HDF

Browse Available: Yes

Additional Product Information: 

trmm.gsfc.nasa.gov

tsdis.gsfc.nasa.gov

DAAC: Goddard Space Flight Center Earth 

Sciences DAAC, daac.gsfc.nasa.gov

Science Team Contact: J. Xiong
54 Data Products Handbook - Volume 1 Terra / ASTER • Radiance and Imagery Products 

ASTER Reconstructed, 

Unprocessed Instrument Data 

(AST01)

Product Description

The ASTER Level 1A raw data (AST01) are reconstructed, unprocessed instrument digital counts with 

ground resolution of 15 m, 30 m, and 90 m for 3 VNIR 

(0.52-0.86 μm), 6 SWIR (1.60-2.43 μm), and 5 TIR 

(8.13-11.65 μm) channels. This product contains depacketized, demultiplexed, and realigned instrument 

image data with geometric correction coefficients and 

radiometric calibration coefficients appended but not 

applied. This includes correcting for SWIR parallax 

as well registration within and between telescopes. 

The spacecraft ancillary and instrument engineering 

data are also included. This product is the responsibility of Japan.

 The radiometric calibration coefficients consisting 

of offset and sensitivity information are generated 

from a database for all detectors. The geometric correction is the coordinate transformation for band-toband coregistration.

Suggested Reading

Yamaguchi, Y. et al., 2001.

ASTER Reconstructed, Unprocessed 

Instrument Data Product Summary

Coverage: Regional up to 780 60 km × 60 

km scenes per day (daytime for all channels, 

daytime and nighttime TIR channels, 

nighttime SWIR and TIR channels for volcano 

observation)

Processing Level: 1A

Spatial/Temporal Characteristics: 15 m 

(VNIR), 30 m (SWIR), 90 m (TIR)

Wavelengths: VNIR–0.52-0.86 μm (3 channels plus 1 stereo channel); SWIR–1.60-2.43 

μm (6 channels); TIR–8.13-11.65 μm (5 

channels)

Product Type: Standard, at-launch

Maximum File Size: 107 MB

File Frequency: N/A

Primary Data Format: HDF-EOS

Browse Available: Yes

Additional Product Information: 

edcdaac.usgs.gov/aster/asterdataprod.html

www.gds.aster.ersdac.or.jp/gds_www2000/

service_eset_service_e.html

DAAC: EDC Land Processes DAAC, 

edcdaac.usgs.gov

Science Team Contact: H. Watanabe
Radiance and Imagery Products • Data Products Handbook - Volume 1 55

ASTER Registered Radiance at 

Sensor (AST03)

Product Description

This Level 1B product (AST03) contains radiometrically calibrated and geometrically coregistered data 

for all ASTER channels. This product is created by 

applying the radiometric and geometric coefficients to 

the Level 1A data. The bands have been coregistered 

both between and within telescopes, and the data have 

been resampled to apply the geometric corrections. As 

for the Level 1A product, these Level 1B radiances 

are generated at 15-m, 30-m, and 90-m resolutions 

corresponding to the VNIR, SWIR, and TIR channels. 

Calibrated, at-sensor radiances are given in W/(m2

μm sr). This product serves as input to derived geophysical products.

Suggested Reading

Yamaguchi, Y. et al., 2001.

ASTER Registered Radiance at Sensor 

Product Summary

Coverage: Regional up to 310 60 km ×

60 km scenes per day (daytime and nighttime)

Processing Level: 1B

Spatial/Temporal Characteristics: 15 m (VNIR), 

30 m (SWIR), 90 m (TIR)

Wavelengths: VNIR–0.52-0.86 μm (3 channels 

plus 1 stereo channel); SWIR–1.60-2.43 μm (6 

channels); TIR–8.13-11.65 μm (5 channels)

Product Type: Standard, at-launch

Maximum File Size: 118 MB

File Frequency: N/A

Primary Data Format: HDF-EOS

Browse Available: Yes

Additional Product Information: 

edcdaac.usgs.gov/aster/asterdataprod.html

DAAC: EDC Land Processes DAAC, 

edcdaac.usgs.gov

Science Team Contact: H. Watanabe

Terra / ASTER 

ASTER Brightness 

Temperature at Sensor 

(AST04)

Product Description

This Level 2 product includes brightness temperature 

in degrees Celsius (C) at 90-m resolution for ASTERʼs 

5 thermal-infrared channels (8-12 μm). This is an onrequest product and is generated only when requested. 

Brightness temperature is the apparent temperature 

of the surface assuming a surface emissivity of 1.0. 

Setting the emissivity to 1.0 is equivalent to assuming 

the target is a black body, so the brightness temperature is defined as the temperature a blackbody would 

have in order to produce the radiance perceived by 

the sensor.

 Brightness temperature has been used to observe 

volcanic ash clouds, detect ice leads in the Arctic, and 

to identify anthropogenic and natural fires, to name 

a few examples. The ASTER brightness temperature 

is used as an alternate to radiance in the temperature/

emissivity separation algorithm to report relative 

cloud top temperature because there is no routinely 

available applicable atmospheric correction to enable 

a calculation of exact cloud-top temperature.

 ASTER brightness temperatures can be acquired 

during the day or night and over all surface types 

(land, water, cloud, etc.).

This is an image of Antelope Valley, California, which was 

acquired by ASTER on May 4, 2001. This image shows the 

brightness temperatures for ASTER Band 11. Prominent features in the scene include the San Andreas Fault, which forms 

the southern boundary of the valley floor, and Castaic Lake, 

the V-shaped reservoir in the lower left corner of the scene. 

On the internet, visit visibleearth.nasa.gov to view a variety 

of color ASTER data images.
56 Data Products Handbook - Volume 1 Terra / ASTER • Radiance and Imagery Products 

ASTER Browse Data –

Decorrelation Stretch Product 

(AST06)

Product Description

This Level 2 browse product contains decorrelation 

stretch images of ASTER VNIR, SWIR, and TIR 

radiance data. These images are produced at pixel 

resolutions of 15 m for VNIR, 30 m for SWIR, and 

90 m for TIR. Decorrelation stretched images provide 

an overview that enhances reflectance and emissivity 

variations and subdues variations due to topography 

and temperature.

Research and Applications

The decorrelation stretch is a process that is used 

to enhance the spectral differences found in multispectral image data sets. These images are used as 

a visual aid in browsing the ASTER scene data and 

making the selection of suitable scenes for further 

analysis and research. In particular, a decorrelationstretched image would show the potential user which 

scenes have spectral variations large enough to be 

useful for subsequent spectral analysis.

Data Set Evolution

The algorithm used removes the interchannel correlation found in the input pixels. The decorrelationstretched images are color coded producing a color 

composite containing the maximum contrast for the 

features in the original set of bands. The user may 

request as an on-demand product a decorrelationstretched image generated from any three ASTER 

channels that share a single telescope (VNIR, SWIR, 

or TIR).

Suggested Reading

Gillespie, A. R. et al., 1986.

Mather, P. M., 1999.

Rothery, D. A., 1987.

ASTER Brightness Temperature at Sensor 

Product Summary

Coverage: Regional up to 70 64 km ×

60 km scenes per day (daytime and nighttime)

Processing Level: 2

Spatial/Temporal Characteristics: 90 m

Wavelengths: Brightness temperatures (°C) 

derived for each thermal infrared channel

Product Type: Standard, at-launch

Maximum File Size: 7 MB

File Frequency: On demand

Primary Data Format: HDF-EOS

Browse Available: Yes

Additional Product Information: 

edcdaac.usgs.gov/aster/asterdataprod.html

DAAC: EDC Land Processes DAAC, 

edcdaac.usgs.gov

Science Team Contact: R. Alley
Radiance and Imagery Products • Data Products Handbook - Volume 1 57

ASTER Browse Data – Decorrelation 

Stretch Product Summary

Coverage: Regional, three images available 

per scene

Processing Level: 2

Spatial/Temporal Characteristics: 15 m (VNIR), 

30 m (SWIR), 90 m (TIR)

Wavelengths: Any three ASTER channels within a single telescope for on-demand products

Product Type: Standard, at-launch

Maximum File Size: 85 MB

File Frequency: On demand

Primary Data Format: HDF-EOS

Browse Available: Yes

Additional Product Information: 

edcdaac.usgs.gov/aster/asterdataprod.html

DAAC: EDC Land Processes DAAC, 

edcdaac.usgs.gov

Science Team Contact: R. Alley

Terra / ASTER 

ASTER Surface Reflectance 

(AST07) and Surface Radiance 

(AST09)

Product Description

The Level 2 surface reflectance data set (AST07) 

contains surface reflectance for VNIR and SWIR 

channels at 15-m and 30-m resolutions, respectively, derived by applying an atmospheric correction 

to observed satellite radiances. The data product is 

recorded as percent reflectance. The method uses a 

look-up table approach based upon a radiative transfer 

model capable of estimating the magnitude of scattering and absorption given atmospheric turbidity 

parameters. The needed section of the lookup table 

is selected using a set of atmospheric parameters (e.g., 

scattering optical depths, columnar ozone, and water 

vapor) known at the time and location of the satellite 

measurements. The retrieved surface reflectance is 

only of known accuracy for cloud-free pixels because 

inputs are known for clear-sky pixels only.

 The Level 2 surface radiance data set (AST09) 

contains surface radiance, in W/(m2 μm sr), for 

VNIR, SWIR, and TIR channels at 15-m, 30-m, 

and 90-m resolutions, respectively. The product is 

derived by applying an atmospheric correction to 

satellite radiances. The atmospheric correction used 

to derive AST09 for the VNIR and SWIR bands is 

similar to that used to drive AST07. The approach for 

atmospheric correction in the thermal infrared uses 

a radiative transfer model capable of estimating the 

magnitude of atmospheric emission, absorption, and 

scattering using a set of atmospheric parameters (e.g., 

temperature, water vapor, ozone, profiles, and scattering optical depths) known at the time and location 

of the satellite measurements. The retrieved surface 

radiance is only of known accuracy for cloud-free 

pixels because inputs are known for clear-sky pixels 

only.

 Both AST07 and AST09 are generated only upon 

request. The products based on VNIR data are available only for daytime satellite overpasses. Those from 

SWIR data are available for daytime overpasses, and, 

in cases of high temperature sources (e.g., volcanoes 

or fires), available for nighttime overpasses. Surface 

radiance from TIR data are available for both daytime 

and nighttime overpasses.

Research and Applications

The objective of these ASTER products is to provide estimates of the surface radiance and reflectance. The thermal infrared radiance includes both 
58 Data Products Handbook - Volume 1 • Radiance and Imagery Products 

surface-emitted and surface-reflected components. 

After accurate atmospheric correction, seasonal and 

annual surface changes can be studied and surface 

kinetic temperature and emissivity can be extracted. 

Surface radiances and reflectances can also be used 

for surface classification, desertification studies, and 

surface energy-balance work.

Data Set Evolution

This product, as well as similar products for other 

Terra instruments, mark the first implementations of 

operational atmospheric corrections in environmental 

satellites. Past work to retrieve surface reflectance 

from satellite imagery has focused on small sets of 

satellite data for highly studied ground targets. The 

primary reason for this is that atmospheric correction 

is a computationally intensive process. Methods for 

reducing the computational requirements include using look-up tables and making assumptions about the 

horizontal homogeneity of the atmosphere.

 Research into developing future versions of code 

to produce AST07 and AST09 focuses on attempting 

pixel-by-pixel corrections using pixel-by-pixel atmospheric input parameters rather than assuming 

horizontal homogeneity. Because of ASTERʼs small 

ground instantaneous field of view, versions of the 

atmospheric correction include corrections for multiple scatter effects from adjacent pixels of differing 

reflectance, also known as the adjacency effect. Lookup tables have also been expanded to include other 

parameters such as surface bidirectional reflectance 

characteristics.

Suggested Reading

Abrams, M. et al., 2000.

Deschamps, P., and T. Phulpin, 1980.

Hilland, J. E. et al., 1985.

Price, J. C., 1984.

Thome, K. et al., 1998a, b.

ASTER Surface Reflectance and Surface 

Radiance Product Summary

Coverage: Regional; 16 days required for 

global coverage; 70 scenes per day

Processing Level: 2

Spatial/Temporal Characteristics: 15 m (VNIR), 

30 m (SWIR), 90 m (TIR)

Wavelengths: VNIR–0.52-0.86 μm (4 channels); SWIR–1.60-2.43 μm (6 channels); 

TIR–8.125-11.65 μm (5 channels)

Product Type: Standard, at-launch

Maximum File Size: 189 MB (VNIR), 

79 MB (SWIR)

File Frequency: On demand

Primary Data Format: HDF-EOS

Browse Available: Yes

Additional Product Information: 

edcdaac.usgs.gov/aster/asterdataprod.html

DAAC: EDC Land Processes DAAC, 

edcdaac.usgs.gov

Science Team Contacts: K. Thome (VNIR, 

SWIR), F. Palluconi (TIR)

Terra / ASTER

These two images are of 

Lake Tahoe, taken on 

27 February 2002, using 

TIR on ASTER, band 

13. The left image represents Surface Leaving 

Radiance, in W/(m2 μm 

sr), and the right image 

represents Sky Irradiance in W/m2/μm.
Radiance and Imagery Products • Terra / ASTER Data Products Handbook - Volume 1 59

ASTER Digital Elevation 

Models (DEMs)(AST14)

Product Description

This data set contains topographic information derived from the along-track, 15-m ASTER optical 

stereo data acquired in the near-infrared. These high 

spatial resolution DEMs (up to 7-m absolute horizontal and vertical accuracy with appropriate ground 

control, and up to 10-m relative accuracy without 

ground control) can be used to derive absolute slope 

and slope aspect good to 5o over horizontal distances 

of more than 100 m. ASTER DEMs meet 1:50,000 

to 1:250,000 map accuracy standards. Postings are 

at 30-m.

 This is an on-request product which is generated 

by the Land Processes DAAC at EROS Data Center 

only when requested and at a rate of 1-2 DEMs per 

day. Based on 3-yearsʼ experience with actual instrument operations, mission planning, cloud cover, 

and illumination, the ASTER digital stereo data with 

a base/height ratio of 0.6 should be acquired for all of 

the Earthʼs land surface below 85o latitude by the end 

of the 5-year mission. These data are appropriate for 

users interested in generating DEMs themselves.

Research and Applications

Topographic data as well as derived slope and 

slope aspect are basic to all aspects of land surface 

research including cartography, climate modeling, 

biogeochemistry, biogeography, geophysics, geology, 

geomorphology and soil science. Digital elevation 

data are also required for atmospheric and radiometric 

correction of most satellite-acquired observations of 

the land surface. Digital elevation data are also used 

for practical engineering applications such as studies 

of drainage and runoff, and site suitability studies for 

buildings or waste containment sites.

Data Set Evolution

Generation of elevation models from stereo photographic data, now a routine adjunct to standard surveying methods, has been developed over the past 

60 years based on the principles of photogrammetry. 

Extensions of these principles to the generation of 

DEMs from optical stereo satellite data have been 

implemented in past decades. Examples of these satellite stereo systems include SPOT and JERS-1 OPS. 

At one time, there were large areas of the globe for 

which no consistent, high-resolution, widely availThis image is a shaded relief of an ASTER 

Digital Elevation Model (DEM). The area 

covers Arequipa, Peru and El Misti volcano. 

Visit visibleearth.nasa.gov to view a variety 

of color ASTER data images.
60 Data Products Handbook - Volume 1 • Radiance and Imagery Products 

able elevation models existed. ASTER DEMs now 

help provide much-needed coverage over many of 

these remote areas.

Suggested Reading

Abrams, M., 2000.

Bohme, R., 1993.

Kaab, A., 2002.

Kahle, A. et al., 1991.

Fujisada, H., 1994.

Fujisada, H., 1998.

Hirano, A. et al., 2003.

OʼNeill, M. A., and I. J. Dowman, 1993.

Toutin, T., 2002.

Welch, R., and H. Lang, 1994.

Yamaguchi, Y. et al., 1993.

Yamaguchi, Y. et al., 1994.

Yamaguchi, Y. et al., 1998.

Terra / ASTER

ASTER Digital Elevation Models (DEMs) 

Product Summary

Coverage: Global

Processing Level: 3

Spatial/Temporal Characteristics: 30 m

Wavelengths: VNIR Band 3 nadir and Band 3 

aft-looking

Product Type: Standard, at-launch

Maximum File Size: 25 MB

File Frequency: On demand

Primary Data Format: HDF-EOS

Browse Available: No

Additional Product Information:

edcdaac.usgs.gov/aster/asterdataprod.html

DAAC: EDC Land Processes DAAC, 

edcdaac.usgs.gov

Science Team Contact: M. Abrams
Radiance and Imagery Products • Data Products Handbook - Volume 1 61

 A complete listing of parameters for this data 

product can be found in the CERES Data Products 

Catalog at asd-www.larc.nasa.gov/DPC/DPC.html, 

and detailed definitions of each parameter can 

be found in the CERES Collection Guide at asdwww.larc.nasa.gov/ceres/collect_guide/list.html.

Research and Applications

These radiance data products are the primary inputs 

to all the CERES product algorithms. The spacecraft 

ephemeris and sensor telemetry are inputs to this subsystem, which uses instrument calibration coefficients 

to convert the spacecraft telemetry inputs into geolocated filtered radiances and housekeeping data into 

engineering units. 

Data Set Evolution

The BDS product algorithm operates on the Level 

0 reformatted raw sensor counts and produces calibrated radiances in the three channels. The steps in 

20 45 70 95 120

Outgoing Longwave Filtered Radiation (Watts per square meter steradian)

TRMM and Terra / CERES 

CERES Bi-Directional Scans 

Product (BDS)

Product Description

The Bi-Directional Scans (BDS) Product contains 

24 hours of instantaneous Level 1B Clouds and the 

Earthʼs Radiant Energy System (CERES) data for 

a single scanner instrument. The BDS contains instantaneous radiance measurements recorded every 

0.01 second for views of space, internal calibration, 

solar calibration, and Earth. It contains all elevation 

scan modes, which include the normal Earth scan 

and the short Earth scan modes and both the fixed 

and rotating azimuth-plane scan modes. The BDS 

also contains Level 0 raw (unconverted) science 

and instrument data as well as the geolocation of the 

radiance measurements. The BDS product contains 

additional data not found in the Level 0 input file, 

including converted satellite position and velocity 

data, celestial data, converted digital status data, and 

parameters used in the equations for radiance count 

conversions.

The image above shows thermal emitted longwave radiance escaping the top of the Earthʼs atmosphere as 

measured by the Clouds and the Earthʼs Radiant Energy System (CERES) instrument on May 25, 2001. 

Record-breaking heat waves in southern Asia, northern Africa, and southwestern U.S. killed dozens of 

people during May. The smallest amounts of radiation emitted to space are over Antarctica where the 

entire surface/atmosphere column is cold, and over deep convective clouds in the tropics seen in this image over Brazil. These clouds have the largest greenhouse effect on the Earth. The highest emission levels 

are from clear hot desert regions during daylight hours. The pronounced banding of the images between 

orbits is produced because the effective radiating temperature of the Earth depends on viewing angle as the 

CERES instrument scans the earth from orbit. On the internet, visit visibleearth.nasa.gov to view a variety 

of CERES data images.
62 Data Products Handbook - Volume 1 TRMM and Terra / CERES • Radiance and Imagery Products 

the processing are: 1) Convert the raw housekeeping 

telemetry into engineering units; 2) calculate the geographical location of the CERES footprints; 3) revise 

the radiometric detector count conversion coefficients 

when required; 4) convert the radiometric detector 

signals into filtered radiances; and 5) archive the BDS 

standard products.

 Documentation on the BDS algorithms can be found 

in the CERES ATBD for Subsystem 1.0, in PDF format, at eospso.gsfc.nasa.gov/atbd/cerestables.html.

Suggested Reading

Hoffman, L. H. et al., 1987.

Jarecke, P. J. et al., 1991.

Lee III, R. B. et al., 1996.

Priestley, K. J. et al., 2000.

Smith, G. L. et al., 1986.

Wielicki, B. A. et al., 1998.

CERES Bi-Directional Scans Product 

Summary

Coverage: Global

Spatial/Temporal Characteristics: 20 km at 

nadir/0.01 second

Key Geophysical Parameters: Filtered total, 

shortwave, and window radiances, geolocation data

Processing Level: 0, 1

Product Type: Standard, at-launch

Maximum File Size: 900 MB

File Frequency: 1/day

Primary Data Format: HDF-EOS

Browse Available: No

Additional Product Information: 

eosweb.larc.nasa.gov/PRODOCS/ceres/

table_ceres.html

DAAC: Langley Atmospheric Sciences Data 

Center, eosweb.larc.nasa.gov

Science Team Contact: K. J. Priestley
Radiance and Imagery Products • Data Products Handbook - Volume 1 63

MISR Reformatted Annotated 

Product (MIS01)

Product Description

The Level 1A Reformatted Annotated Product 

contains raw MISR data which are decommutated, 

reformatted (12-bit L0 data shifted to byte boundaries, reversal of square-root encoding applied, and 

converted to 16-bits) and annotated (e.g., with time 

information, etc.). The MISR Level 1A data set is the 

primary archive of MISR instrument Charge-Coupled 

Device (CCD) science data, instrument engineering 

data (e.g., instrument temperatures, electrical status, 

command verification, etc.), calibration data, motor 

data, and navigation data (raw spacecraft position 

and attitude). These data are used by the Level 1B1 

processing algorithm to generate calibrated radiances. 

The science data output preserves the spatial sampling 

rate of the Level 0 raw MISR CCD Science data.

 CCD data are collected during routine science 

observations of the sunlit portion of the Earth. Each 

product represents one “granule” of data. A “granule” 

is defined to be the smallest unit of data required 

for MISR processing. Also included in the Level 1A 

product will be pointers to calibration coefficient files 

provided for Level 1B processing.

Suggested Reading

Bruegge, C. J. et al., 2002.

MISR Reformatted Annotated Product 

Summary

Coverage: Daytime; 378-km swath width (nadir), 413-km swath width (off nadir), providing 

global coverage in 9 days

Processing Level: 1A

Spatial/Temporal Characteristics: The spatial 

sampling of the nadir-viewing camera is 250 m 

(cross-track) × 275 m (along-track), while for 

the 8 off-nadir cameras the sampling is 275 m 

in both directions. Onboard averaging up to 1.1 

km is selectable by ground command.

Wavelengths: 446, 558, 672, 866 nm

Product Type: Standard, at-launch

Maximum File Size: 900 MB (nadir), 

400 MB (oblique)

File Frequency: 135/day

Primary Data Format: HDF-EOS Swath

Browse Available: No

Additional Product Information: 

eosweb.larc.nasa.gov/PRODOCS/misr/table_

misr.html

DAAC: Langley Atmospheric Sciences Data 

Center, eosweb.larc.nasa.gov

Science Team Contact: C. Bruegge

Terra / MISR 
64 Data Products Handbook - Volume 1 Terra / MISR • Radiance and Imagery Products 

MISR Radiometric Product

(MIS02)

Product Description

The MISR Radiometric Product (MIS02) is produced 

during Level 1B1 processing. Here Level 1A data 

are converted from digital counts to radiances, using 

coefficients that are updated periodically to account 

for instrument degradation. The Radiometric Product 

contains spectral radiances for all MISR channels 

(four spectral bands and nine cameras), given in units 

of W/(m2 μm sr). Each radiance value represents the 

incident radiance averaged over the sensorʼs total 

band response. The radiometric coefficients are derived primarily from use of the On-Board Calibrator 

(OBC). The OBC contains Spectralon calibration 

panels which are deployed at bimonthly intervals and 

reflect sunlight into the cameras. Detector standards 

measure this reflected light to provide the calibration. 

Vicarious field campaigns are additionally used to 

adjust the radiometric scale. These experiments are 

conducted less frequently, but provide an independent 

methodology useful for reducing systemic errors.

Suggested Reading

Bruegge, C. J. et al., 1998.

Jovanovic, V. M. et al., 2002.

MISR Radiometric Product Summary

Coverage: Daytime; 378-km swath width (nadir), 413-km swath width (off nadir), providing 

global coverage in 9 days

Processing Level: 1B1

Spatial/Temporal Characteristics: The spatial 

sampling of the nadir viewing camera is 250 m 

(cross-track) × 275 m (along-track), while for 

the 8 off-nadir cameras the sampling is 275 m 

in both directions. Onboard averaging up to 1.1 

km is selectable by ground command.

Wavelengths: 446, 558, 672, 866 nm

Product Type: Standard, at-launch

Maximum File Size: 1300 MB (nadir), 400 MB 

(oblique), Local Mode Radiance Data: 30 MB

File Frequency: 135/day, Local Mode Radiance 

Data, 15/day

Primary Data Format: HDF-EOS Swath

Browse Available: No

Additional Product Information: 

eosweb.larc.nasa.gov/PRODOCS/misr/table_

misr.html

DAAC: Langley Atmospheric Sciences Data 

Center, eosweb.larc.nasa.gov

Science Team Contact: C. Bruegge
Radiance and Imagery Products • Data Products Handbook - Volume 1 65

MISR Geo-rectified Radiance 

Product (MIS03)

Product Description

The Geo-rectified Radiance Product (GRP) consists 

of parameters that have had geometric corrections 

applied and have been projected to a Space-Oblique 

Mercator (SOM) map grid. Included in this product 

is the surface-projected TOA radiance, which is the 

calibrated radiance from the Level 1B1 data (MIS02) 

that has had a geometric correction applied to remove 

spacecraft position and pointing knowledge errors as 

well as effects due to topography. The radiance is then 

orthorectified on a reference ellipsoid at the surface. 

Also part of the GRP is the ellipsoid-projected TOA 

radiance that uses supplied spacecraft position and 

pointing and is not corrected for topography, but is 

resampled at surface reference ellipsoid. In addition, 

geometric parameters such as solar and view zenith 

and azimuth angles are included.

 Resampling of MISR data at Level 1B2 is critical because the pushbroom images from the nine 

cameras are obtained at widely separated locations 

along the subspacecraft track. However, derivation 

of geophysical products requires that the multiangle, 

multispectral radiances for any single ground target 

be coregistered.

Suggested Reading

Jovanovic, V. M. et al., 1998.

MISR Geo-rectified Radiance Product 

Summary

Coverage: Daytime, 378-km swath width (nadir), 413-km swath width (off nadir), providing 

global coverage in 9 days

Processing Level: 1B2

Spatial/Temporal Characteristics: The resampled data are provided on a 275 m × 275 m 

Space Oblique Mercator grid in certain channels, and a 1.1 km × 1.1 km grid in the remaining channels, as established by the instrument 

observing configuration.

Wavelengths: 446, 558, 672, 866 nm

Product Type: Standard, at-launch

Maximum File Size:

Ellipsoid Data: 600 MB (nadir), 

200 MB (oblique)

Terrain Data: 350 MB (nadir), 

100 MB (oblique)

Geometric Parameters: 6 MB

Radiometric Cloud Mask: 10 MB

File Frequency: 135/day, 15/day (geometric 

parameters)

Primary Data Format: HDF-EOS Swath

Browse Available: Yes

Additional Product Information:

eosweb.larc.nasa.gov/PRODOCS/misr/table_

misr.html

DAAC: Langley Atmospheric Sciences Data 

Center, eosweb.larc.nasa.gov

Science Team Contact: D. Diner

MISR Geo-rectified Radiance Products over Delaware Bay, 

Chesapeake Bay, and the Appalachian Mountains acquired 

on March 24, 2000 during Terra orbit 1417. The larger image 

was taken by the MISR camera viewing straight down (nadir). 

The series of smaller images, from top to bottom, respectively, 

were taken by cameras viewing 70.5o forward, 45.6o forward, 

45.6o aftward, and 70.5o aftward of nadir. These images cover 

the environs of Newark, Philadelphia, Baltimore, Washington 

and Richmond. Differences in brightness and contrast as a 

function of view angle are visible over both land and water. 

On the internet, visit visibleearth.nasa.gov to view a variety 

of MISR data images. 

Terra / MISR 
66 Data Products Handbook - Volume 1 • Radiance and Imagery Products 

MISR Ancillary Radiometric 

Product (MIS11)

Product Description

The MISR Ancillary Radiometric Product (ARP) contains coefficients and data variables that are used in 

the Level 1B1 processing. Updated ARP parameters 

include the sensor radiometric calibration coefficients, 

uncertainties in calibration, signal-to-noise ratios, 

pixel data quality indicators, and quality assessment 

threshold parameters. Static ARP parameters include 

spectral response parameters, point-spread-functions 

(PSF), fields-of-view, passband-weighted solar irradiance values, and photosynthetically active radiation 

(PAR) integration weights. The ARP is regenerated 

periodically at the MISR SCF to update the instrument performance report. The ARP is used as input 

to Level 1B1 as well as Level 2 processing.

MISR Ancillary Radiometric Product 

Summary

Coverage: N/A, generated periodically

Spatial/Temporal Characteristics: Per pixel

Key Parameters: Radiometric calibration coefficients, uncertainties, signal-to-noise ratios, 

spectral response parameters, PSF, fields-ofview, band-averaged solar irradiance, and PAR 

weights

Product Type: Internal

Maximum File Size: 5 MB

File Frequency: N/A

Primary Data Format: HDF-EOS Grid

Browse Available: No

Additional Product Information:

eosweb.larc.nasa.gov/PRODOCS/misr/table_

misr.html

DAAC: Langley Atmospheric Sciences Data 

Center, eosweb.larc.nasa.gov

Science Team Contact: C. Bruegge

Terra / MISR

MISR Ancillary Geographic 

Product (MIS10)

Product Description

The MISR Ancillary Geographic Product (AGP) contains terrain data (generated from a high resolution 

DEM), referenced to the WGS84 reference ellipsoid 

and mapped onto a SOM grid. It is an archival product 

generated once preflight at the MISR Science Computing Facility (SCF), but which can be distributed 

to the scientific community through the DAAC as 

an aid in interpreting MISR retrievals. The AGP is 

used as input to Level 1B2 and Level 2 processing. 

Its contents include latitude, longitude, scene elevation (average and standard deviation relative to a 

horizontal plane and the local slope), surface-normal 

zenith angle, surface-normal azimuth angle, and a 

land/ocean/inland water/ephemeral water/coastline 

mask. All of these parameters are given on 1.1-km 

centers. Average and standard deviation of scene 

elevation over 17.6-km areas are also provided.

MISR Ancillary Geographic Product 

Summary

Coverage: Global, one time only

Spatial/Temporal Characteristics: 1.1 km for 

most parameters, 17.6 km for coarse-resolution elevation information

Wavelengths: N/A, parameters include latitude, 

longitude, elevation, and land/water classification

Product Type: Internal, on request

Maximum File Size: 110 MB

File Frequency: N/A

Primary Data Format: HDF-EOS Grid

Browse Available: No

Additional Product Information:

eosweb.larc.nasa.gov/PRODOCS/misr/table_

misr.html

DAAC: Langley Atmospheric Sciences Data 

Center, eosweb.larc.nasa.gov

Science Team Contact: D. Diner
Radiance and Imagery Products • Data Products Handbook - Volume 1 67

MODIS Level 1B Calibrated, 

Geolocated Radiances 

(MOD02)

Product Description

The Level 1B data set contains calibrated and geolocated output for the 36 spectral bands generated 

from MODIS Level 1A sensor counts (MOD01). The 

primary product of the reflected solar bands (1-19 

and 26) is the Earth view (EV) reflectance factor, 

ρEVcos(θEV). The primary product for the thermal 

emissive bands (20-25 and 27-36) is spectral radiance. 

The radiances are in W/(m2 μm sr). In addition, EV 

spectral radiance may be determined for the solar reflective bands through knowledge of the solar irradiance. Additional data are provided, including quality 

flags, error estimates, and calibration data.

 Visible, SWIR, and NIR measurements are made 

during daytime only, while radiances for TIR are 

measured continuously. Only Channels 1 and 2 

have 250-m resolution, Channels 3-7 have 500-m 

resolution, and the rest have 1-km resolution.

MODIS Level 1B Calibrated, Geolocated 

Radiances Summary

Coverage: Global

Spatial/Temporal Characteristics:

0.25, 0.5, and 1 km resolutions/daily (daytime 

and nighttime)

Wavelengths: 20 bands 0.4-3.0 μm, 

16 bands 3-15 μm

Processing Level: 1B

Product Type: Standard, at-launch

Maximum File Size: 345 MB (1 km), 

276 MB (500 m), 287 MB (250 m)

File Frequency: 288/day (1 km), 144/day 

(500 m), 144/day (250 m)

Primary Data Format: HDF-EOS

Additional Product Information: 

daac.gsfc.nasa.gov/MODIS/

DAAC: Goddard Space Flight Center Earth 

Sciences DAAC, daac.gsfc.nasa.gov

Science Team Contacts: X. Xiong

MODIS Level 1A Radiance 

Counts (MOD01)

Product Description

This Level 1A data set contains counts for 36 MODIS 

spectral bands, along with raw instrument engineering 

and spacecraft ancillary data. The Level-1A data are 

used as input for geolocation, calibration, and processing. Quality indicators are added to the data to 

indicate missing or bad pixels and instrument modes. 

Visible, SWIR, and NIR measurements are made 

during daytime only, while radiances for TIR are 

measured during both the day and the night portions 

of the orbit. This product includes all MODIS data in 

digitized (counts) form for all bands, all spatial resolutions, all time tags (converted), all detector views 

(Earth, solar diffuser, spectroradiometric calibration 

assembly, black body, and space view), and all engineering and ancillary data.

MODIS Level 1A Radiance Counts 

Summary

Coverage: Global

Spatial/Temporal Characteristics:

0.25, 0.5, and 1 km resolutions/ daily (daytime 

and nighttime)

Wavelengths: 20 channels 0.4-3.0 μm, 

16 channels 3-15 μm

Processing Level: 1A

Product Type: Standard, at-launch

Maximum File Size: 534 MB

File Frequency: 288/day

Primary Data Format: HDF-EOS

Additional Product Information: 

daac.gsfc.nasa.gov/MODIS/

DAAC: Goddard Space Flight Center Earth 

Sciences DAAC, daac.gsfc.nasa.gov

Science Team Contact: V. V. Salomonson

Terra / MODIS 
68 Data Products Handbook - Volume 1 Terra / MODIS • Radiance and Imagery Products 

MODIS Geolocation Data Set 

(MOD03)

Product Description

The MODIS Geolocation product contains geodetic 

coordinates, ground elevation, land/sea mask, satellite 

range, and solar and satellite zenith, and azimuth 

angle for each MODIS 1-km sample. These data are 

provided as a ʻcompanionʼ data set to the Level 1B 

calibrated radiances and the Level 2 data sets to enable further processing. These geolocation fields are 

determined using the spacecraft attitude and orbit, 

instrument telemetry, and a digital elevation model. 

Additional data are provided, including quality flags 

and error estimates.

Suggested Reading

Wolfe, R. E. et al., 2002.

MODIS Geolocation Data Set Summary

Coverage: Global

Spatial/Temporal Characteristics: 

1 km resolution/daily (daytime and nighttime)

Wavelengths: N/A

Processing Level: 1B

Product Type: Standard, at-launch

Maximum File Size: 60 MB

File Frequency: 288/day

Primary Data Format: HDF-EOS

Additional Product Information: 

daac.gsfc.nasa.gov/MODIS/Terra/rad_geo/

MOD03.shtml

DAAC: Goddard Space Flight Center Earth 

Sciences DAAC, daac.gsfc.nasa.gov

Science Team Contacts: 

V. V. Salomonson, R. E. Wolfe, A. J. Fleig

The images above show the junction of the Ohio River 

and the Mississippi River where Spring floods were 

at their worse. The images compare April 25, 2002, to 

May 18, 2002, with data from MODIS. The Mississippi 

River rose up to 12 feet above flood stage in the area 

shown here. Normally, all the rivers in this image 

would resemble thin black lines (top image). Image 

courtesy Jacques Descloitres, MODIS Land Rapid Response Team at NASA Goddard Space Flight Center. 

On the internet, visit visibleearth.nasa.gov to view a 

variety of MODIS data images.

April 25, 2002

May 18, 2002
Radiance and Imagery Products • Data Products Handbook - Volume 1 69

sources of uncertainty in the product: 1) The candidate aerosol models chosen to describe the aerosol 

may be unrepresentative of the natural aerosol, e.g., 

wind-blown dust; 2) there is uncertainty in the estimate of the whitecap reflectance/radiance, 3) there 

is uncertainty in the sensorʼs radiometric calibration, 

and 4) there are unaccounted bi-directional effects 

related to the angular distribution of upwelling radiance just beneath the sea surface. Efforts to reduce 

these uncertainties include development of improved 

aerosol models, direct measurements of the properties of oceanic whitecaps, utilization of the Marine 

Optical Buoy (MOBY) calibration site to improve 

radiometric calibration, and direct measurement of 

the upwelling radiance distribution at the MOBY site 

and during validation experiments. The product is 

validated by comparing simultaneous surface-based 

measurements (including drifting-buoy radiometers) 

and MODIS-derived values at a set of locations. The 

validation activity will continue throughout the mission to assure stability of this data product, and all 

other ocean products derived from the water-leaving 

radiances.

Suggested Reading

Chomko, R., and H. R. Gordon, 1998.

Deschamps, P. Y. et al., 1983.

Evans, R. H., and H. R. Gordon, 1994.

Gordon, H. R., 1997.

Gordon, H. R., and D. K. Clark, 1981.

Gordon, H. R., and A. Y. Morel, 1983.

Gordon, H. R., and M. Wang, 1994.

Gordon, H. R. et al., 1997.

Morel, A., and B. Gentili, 1991.

Morel, A., and B. Gentili, 1993.

Morel, A., and B. Gentili, 1996.

Morel, A. et al., 1995.

MODIS Normalized WaterLeaving Radiance (MOD18) 

and Aerosol Optical Depth 

(MOD37)

Product Description

This Level 2 and Level 3 product contains ocean 

water-leaving radiances for bands 8-14 of MODIS 

(wavelengths 412-681 nm). These are the “ocean” 

bands; the water-leaving radiances in these bands are 

used to derive nearly all of the MODIS ocean products. In addition, another key parameter generated by 

the algorithm is provided as product MOD37: Aerosol Optical Depth. The Level 2 product is provided 

daily at 1-km resolution for cloud-free pixels, while 

the Level 3 product is provided daily, 8-day weekly, 

monthly, and yearly. The product constraints are that 

only cloud-free pixels are used (with Sun glitter below 

a threshold), and all valid pixels are outside a distance 

threshold from land.

Research and Applications

Normalized Water-Leaving Radiance is approximately the radiance that would exit the ocean in the 

absence of the atmosphere, if the Sun were at the 

zenith. It is used in the bio-optical algorithms to estimate chlorophyll a concentration and ocean primary 

productivity on a global scale. The algorithm evolved 

from experience from the CZCS experiment, which 

proved the feasibility of measuring ocean color from 

space. Extensive testing of the algorithm has been 

conducted using SeaWiFS imagery and is continuing 

using the Terra and Aqua MODIS instruments. This 

is the fundamental product for recovering most of the 

MODIS ocean products. 

Data Set Evolution

Inputs to the algorithm are the Level 1 radiances 

in Bands 8-14, screened for clouds and land, and 

estimates of the surface wind speed, atmospheric 

pressure, and ozone concentration derived from National Centers for Environmental Prediction (NCEP, 

formerly the NMC) products. The success of the 

algorithm depends on the accurate characterization 

of the atmospheric effect, which typically constitutes 

90 percent of the at-satellite radiance over the ocean. 

The algorithm includes single scattering, multiple 

scattering effects, and whitecap removal, and uses 

six types of ancillary data. There are three major 

Terra / MODIS
70 Data Products Handbook - Volume 1 Terra / MODIS • Radiance and Imagery Products 

MODIS Normalized Water-Leaving Radiance 

and Aerosol Optical Depth Product 

Summary

Coverage: Global ocean surface, clear-sky 

only

Spatial/Temporal Characteristics: 1 km/daily 

(Level 2), 4.6 km, 36 km, 1°/daily, 8-day, 

monthly, yearly (Level 3)

Key Science Applications: Ocean chlorophyll, 

ocean productivity

Key Geophysical Parameters: Water-leaving 

radiances in the ocean bands, aerosol optical 

depth

Processing Level: 2, 3

Product Type: Standard, at-launch

Maximum File Size: 88 MB (Level 2); 640 MB 

binned, 134 MB mapped (Level 3)

File Frequency: 144/day (Daily Level 2); 7/day 

(Daily Level 3), 7/8-day (8-day Level 3), 

7/month (Monthly Level 3),

7/year (Yearly Level 3)

Primary Data Format: HDF-EOS

Additional Product Information: 

modis-ocean.gsfc.nasa.gov/dataprod.html

DAAC: Goddard Space Flight Center Earth 

Sciences DAAC, daac.gsfc.nasa.gov

Science Team Contacts: H. R. Gordon,

K. Voss

MODIS Photosynthetically 

Active Radiation

(PAR)(MOD22)

Product Description

This Level 2 and 3 product consists of two parameters 

related to the solar radiation entering the ocean that 

is available for photosynthesis. The first is Instantaneous Photosynthetically Active Radiation (IPAR), 

which is the total downwelling flux of photons just 

below the sea surface at the instant MODIS views the 

pixel, integrated over the wavelength range of 400 to 

700 nm. The second is Absorbed Radiation by Phytoplankton (ARP) averaged over the first optical depth 

at 685 nm. This parameter is used by the MOD20 

product in the calculation of fluorescence efficiency. 

This product is produced at 1 km daily for Level 2 

and gridded at 4.6 km, 36 km, and 1° daily, 8-day 

weekly, monthly and yearly for Level 3.

Research and Applications

Knowledge of the photosynthetically available radiation (PAR) is critical for determining the photosynthetic rate of phytoplankton, and thus for estimating 

ocean primary production. Primary productivity 

algorithms use a variety of forms of PAR (Behrenfeld and Falkowski, 1997). The most highly resolved 

models require an instantaneous PAR, such as that 

provided by this product. Other algorithms, such as 

those used in the MODIS Primary Productivity product (MOD27), use a daily integrated PAR. For this 

product, it was decided to provide only instantaneous 

PAR (IPAR) since measurements are available only 

at the time of the satellite overpass. Having two MODIS instruments in orbit, together with other similar 

satellite missions (SeaWiFS, MERIS, GLI), the time 

course of IPAR throughout the day can be estimated to 

compute daily primary productivity using the highly 

resolved models.

 Radiation absorbed by the phytoplankton (ARP) 

is utilized for photosynthesis, fluorescence, and heat. 

Efficiencies of these processes are determined by dividing their respect rates by ARP. The Chlorophyll 

Fluorescence product (MOD20) uses ARP together 

with the Fluorescence Line Height to estimate a Fluorescence Efficiency, with the goal of relating this to 

photosynthetic efficiency (see MOD20). Neglecting 

variations in the heating efficiency, photosynthetic 

efficiency is inversely related to the fluorescence 

efficiency. 
Radiance and Imagery Products • Data Products Handbook - Volume 1 71

MODIS Photosynthetically Active Radiation 

Summary

Coverage: Global ocean surface, clear-sky 

only

Spatial/Temporal Characteristics: 1 km/daily 

(Level 2); 4.6 km, 36 km, 1°/daily, 8-day, 

monthly, yearly (Level 3)

Key Science Applications: Ocean chlorophyll, 

productivity

Key Geophysical Parameters: Photosynthetically available irradiance

Processing Level: 2, 3

Product Type: Validation, at-launch

Maximum File Size: 83 MB (Level 2);

640 MB binned, 134 MB mapped

(Level 3)

File Frequency: 144/day (Daily Level 2); 

2/day (Daily Level 3), 2/8-day (8-day Level 3), 

2/month (Monthly Level 3), 2/year (Yearly Level 

3)

Primary Data Format: HDF-EOS

Browse Available: 36-km sample imagery 

available at the GES DAAC (Level 3 only)

Additional Product Information:

modis-ocean.gsfc.nasa.gov/dataprod.html

DAAC: Goddard Space Flight Center Earth 

Sciences DAAC, daac.gsfc.nasa.gov

Science Team Contacts: K. Carder, D. Tanré, 

W. Esaias, M. Abbott

Data Set Evolution

The model of Gregg and Carder (1990) is used to 

estimate the downwelling solar irradiance at the 

ocean surface. Product generation begins with solar-irradiance data at 1-nm resolution taken from 

the revised Neckel and Labs data and corrected for 

Earth-Sun distance for the current day. Atmospheric 

correction is made for the effects of scattering, 

absorption by ozone, absorption by gas, and water 

vapor. This spectrum is then reflected and refracted 

at the air-sea interface and the below-surface values are computed and summed to give IPAR. The 

phytoplankton absorption spectrum is then applied 

to IPAR to obtain ARP. Inputs to the algorithm are 

Water-Leaving Radiance (MOD18) and Absorption 

Coefficients (MOD36). 

Suggested Reading

Behrenfeld, M. J., and P. F. Falkowski, 1997b.

Gordon, H. R., and M. Wang, 1994.

Gregg, W. W., and K. L. Carder, 1990.

Iqbal, M., 1983.

Paltridge, G. W., and C. M. R. Platt, 1976.

Neckel, H., and D. Labs, 1984.

Thomas, D. et al., 2002.

Terra / MODIS
72 Data Products Handbook - Volume 1 Terra / MODIS • Radiance and Imagery Products 

MODIS Processing Framework 

and Match-up Database 

(MOD32)

Product Description

This Level 2 product consists of the calibration data set 

to be used in the generation of all the MODIS Ocean 

products. It consists of a database which contains in 

situ observations of ocean parameters matched with 

satellite measurement data. This matchup database 

was initially populated with existing ocean-surface 

data matched temporally and spatially with CZCS, 

AVHRR, and SeaWiFS data, and then with MODIS 

data postlaunch. The product includes the definition 

of the processing framework in which all the MODIS 

ocean product algorithms will operate. It is produced 

daily and supports ocean products at 1 km, but the 

product itself does not have a spatial resolution.

Research and Applications

This is a calibration product for MODIS ocean processing. It is used through a vicarious calibration 

scheme to update MODIS radiometric calibration 

coefficients, which directly relate MODIS Level 1A 

raw counts to Level 2 calibrated water-leaving radiances, and to monitor long-term performance of the 

MODIS instrument. MOD32, as a match-up database, 

is also used to validate derived ocean geophysical 

parameters such as SST and for quality control of 

the ocean product suite.

Data Set Evolution

Heritage programs provide the basis for MODIS 

algorithms. A program developed for the Pathfinder 

ocean SST product forms the framework for analyzing AVHRR-derived SST, algorithm development 

and validation, and application of the match-up database. Development for ocean-color algorithms was 

based on experience gained during the transition from 

CZCS to SeaWiFS algorithms. Development of the 

SeaWiFS program involved integration of algorithms 

generated by H. Gordon (atmospheric correction) and 

K. Carder (chlorophyll). In addition, daily validity 

tests were developed through collaboration of the 

SeaWiFS calibration/validation team and the MODIS Ocean Science Team members. To create the 

match-up database, in situ records are first temporally 

matched against the satellite retrievals. The satellite 

data for the match-up database are extracted for 3 ×

3 pixel boxes centered at each in situ measurement 

location. Sea-surface temperature observations are 

from two main types of platforms: moored buoys 

and drifting buoys. The primary source of matchup bio-optical measurements is the Marine Optical 

Buoy (MOBY), but measurements made from ship 

surveys are also included in this database. Prior to 

the launch of MODIS, algorithms were validated using data from the SeaWiFS and AVHRR Pathfinder 

programs. Computer codes were then generated 

to produce each algorithm product, in accordance 

with EOS coding standards and accounting for the 

sequence of data production required by product 

interdependencies. 

Suggested Reading

Brown, O. et al., 2002.

Evans, R. H., and H. R. Gordon, 1994.

Gordon, H. R., 1987.

Gordon, H. R., and A. Y. Morel, 1983.

Kilpatrick, K. A. et al., 2001.

Kearns, E. et al., 2001.

Slater, P. N. et al., 1987.

MODIS Processing Framework and Matchup Database Product Summary

Coverage: Global ocean surface

Spatial/Temporal Characteristics: 1 km/daily

Key Science Applications: Ocean chlorophyll, 

ocean productivity, all ocean products

Key Geophysical Parameters: Match-up 

databases, water-leaving radiances, in situ 

measurements

Processing Level: 2

Product Type: Standard, at-launch

Science Team Contacts: R. H. Evans, H. R. 

Gordon
Radiance and Imagery Products • Data Products Handbook - Volume 1 73

Suggested Reading

Carder, K. L. et al., 1991.

Carder, K. L., 2001.

Carder, K. L., 2002.

Gordon, H. R., 1978.

Gordon, H. R., and D. K. Clark, 1981.

Gordon, H. R., and A. Y. Morel, 1983.

Gordon, H. R., and M. Wang, 1994.

MODIS Clear-Water Epsilon Summary

Coverage: Global ocean surface, clear-sky 

only

Spatial/Temporal Characteristics: 1 km/daily 

(Level 2); 4.6 km, 36 km, 1°/daily, 8-day, 

monthly, yearly (Level 3)

Key Science Applications: Aerosol iron estimation, water-leaving radiance correction

Key Geophysical Parameters: Clear-water 

epsilon, aerosol iron content

Processing Level: 2, 3

Product Type: Validation, at-launch

Maximum File Size: 88 MB (Level 2);

865 MB binned, 134 MB mapped 

(Level 3)

File Frequency: 144/day (Daily Level 2); 36/day 

(Daily Level 3), 36/8-day (8-day Level 3),

36/month (Monthly Level 3), 36/year (Yearly 

Level 3)

Primary Data Format: HDF-EOS

Browse Available: 36-km sample imagery 

available at the GES DAAC (Level 3 only)

Additional Product Information:

modis-ocean.gsfc.nasa.gov/dataprod.html

DAAC: Goddard Space Flight Center Earth 

Sciences DAAC, daac.gsfc.nasa.gov

Science Team Contact: K. Carder

MODIS Clear-Water Epsilon 

(MOD39)

Product Description

This product provides a single parameter, the ratio of 

clear-water-leaving radiance at 531 nm to that at 667 

nm and is called the clear-water epsilon. This quantity 

relates directly to the iron content of aerosols over 

clear waters mostly in the ±35° latitude range. The 

Level 2 product is produced daily, at 1 km spatial 

resolution, whereas the Level 3 product is produced 

daily, 8-day weekly, monthly, and yearly, at 4.6 km, 

36 km, and 1° resolution. 

Research and Applications

The primary purpose of this algorithm is to estimate 

aerosol iron content over ocean waters. The aerosol 

iron influences the validity of other MODIS products. The secondary objective is to flag instances 

when normalized water-leaving-radiance retrievals 

may need adjustment due to aerosol absorption at 

blue and green wavelengths. Such errors will affect 

chlorophyll a calculations. The third objective is to 

provide a check on the Angstrom exponent derived at 

red and infrared wavelengths. The algorithm is valid 

for pigment concentrations up to 2 mg/m3. When pigment concentrations are larger than this, the algorithm 

can no longer be applied.

Data Set Evolution

The algorithm is based on methods developed for 

obtaining clear-water epsilon values from CZCS data. 

Modifications for the MODIS algorithm include extension of the clear-water concept to include waters 

with higher pigment concentrations and modification 

of the values of the normalized water-leaving radiance 

at 520, 550, and 670 nm for CZCS to the slightly 

different MODIS bands by means of the water-absorption curve. Product validation used SeaWiFS data 

in the pre-launch period and MODIS data postlaunch. 

Scattering and optical-thickness data plus ship data 

of water-leaving radiances are acquired to test the 

clear-water radiance assumptions. 

Terra / MODIS
74 Data Products Handbook - Volume 1 Terra / MOPITT • Radiance and Imagery Products

MOPITT Geolocated Radiances 

(MOP01)

Product Description

This product contains geolocated Level 1B radiances 

for the eight spectral channels of the MOPITT instrument. Radiances are obtained at three different 

wavelengths using two different methods of gas 

correlation radiometry. Reflected solar radiation is 

measured at 2.2 and 2.3 μm using length-modulated 

cells to determine the total atmospheric columns of 

methane and carbon monoxide respectively. Thermal radiation at 4.6 μm is measured in four channels 

using a combination of length-modulated and pressure-modulated cells to provide information on the 

vertical profile of carbon monoxide. Engineering data 

describing the state of the gas cells and calibration 

are also provided to support transformation of the 

radiances into Level 2 products.

 Radiances are acquired by cross-track scanning 

about the nadir resulting in an interlaced swath of 

pixels approximately 640 km wide. All channels are 

simultaneously measured on four separate pixels 

each with a resolution of 22 × 22 km. Each channel 

measures radiance in two states of its correlation cell. 

The states correspond to long and short optical paths 

through the gas cell. An average and a difference 

radiance are derived from these two.

Suggested Reading

Deeter, M. N. et al., 2002.

MOPITT Science Team, 1996.

MOPITT Geolocated Radiances Product 

Summary

Coverage: Global

Spatial/Temporal Characteristics: 650-km 

swath centered at nadir; interlaced crosstrack 

scan of 4 pixels, each 22 × 22 km at nadir

Wavelengths: 

2.258 μm center, ±0.355 width

2.334 μm center, ±0.011 width

4.627 μm center, ±0.055 width

Processing Level: 1B

Product Type: Standard, at-launch

Maximum File Size: 83 MB

File Frequency: 1/day

Primary Data Format: HDF-EOS

Browse Available: No

Additional Product Information: 

www.eos.ucar.edu/mopitt/index.html

www.atmosp.physics.utoronto.ca/MOPITT/

home.htm

DAAC: Langley Atmospheric Sciences Data 

Center, eosweb.larc.nasa.gov

Science Team Contact: J. R. Drummond
75

Chapter 5

Precipitation,

Atmospheric Humidity

and Atmospheric

Temperatures

TRMM

PR

TMI

Terra

MODIS
76 Data Products Handbook - Volume 1 Overview • Precipitation, Atmospheric Humidity and Atmospheric Temperatures 

Precipitation, Atmospheric Humidity and Atmospheric 

Temperatures – An Overview

is dominated by tropical Pacific ocean-atmosphere interactions. These interactions are associated with the 

El Niño-Southern Oscillation (ENSO) cycle, whose 

major swings, which occur at irregular intervals of 

two to seven years, are associated with pronounced 

year-to-year precipitation variations over large areas 

of the tropics. The large-scale spatial coherence and 

systematic evolution of both intraseasonal and interannual tropical precipitation anomalies is of great 

significance to climate prediction. They are also 

important in the development of optimal sampling 

strategies for estimating accumulated precipitation 

over periods of a few weeks to a few months.

 The characterization of rainfall in tropical regimes is 

fundamental to improving techniques of radar-rainfall 

measurements as well. Not only are there regional and 

seasonal differences, but significant differences also 

occur within tropical rain systems. These differences 

can be broadly classified as 1) morphological, referring to sizes, lifetimes, and spatial structures of rainproducing systems; and 2) microphysical, referring to 

raindrop formation processes, and the raindrop size 

distribution. Rainfall rates display a highly variable 

structure in both time and space, meaning radar surveillance systems must take frequent measurements 

to accurately assess rainfall totals. Three-dimensional 

volume scans (like the WSR-88D radar scans on the 

nightly weather reports) every five minutes are desirable, although scans at 15-minute intervals provide 

useful data. The hydrological and severe weather 

monitoring requirements of most operational radar 

systems satisfy these criteria, providing invaluable 

ground-based validation data with nominal additional 

expenditure.

Humidity: Atmospheric circulation redistributes 

moisture and heat. Water vapor evaporates from the 

ocean and land surfaces, and is transported to other 

parts of the atmosphere where condensation occurs, 

clouds form, and precipitation ensues. There is a net 

outflow of atmospheric moisture from the tropics, 

where sea-surface temperatures are high and the 

warm atmosphere can hold large amounts of water 

vapor, to higher latitudes, where condensation and 

precipitation remove the moisture. Current numerical 

weather prediction models give inconsistent results 

about the future distribution of precipitation.

 Humidity is the amount of water vapor in the 

atmosphere. Water vapor concentration depends on 

temperature, which determines the total amount of 

Relationship to Global Change Issues

The hydrological cycle is one of three key Earth 

system processes that are components of global 

climate; the carbon and energy cycles are the others. 

Monitoring precipitation and atmospheric humidity 

is critical to obtaining hydrological-cycle parameters 

needed for developing global climate models and detecting climate change. The processes that generate 

rainfall are central to the dynamical, biological, and 

chemical processes in the atmosphere, in the oceans, 

and on the land surfaces. Space-based measurements 

give a more accurate global documentation of tropical rainfall. The TRMM instruments are designed to 

provide greatly-improved measurements of rain rates 

over both land and ocean compared to current methods. TRMM, often referred to as a “flying rain gauge,” 

makes an important contribution to the World Climate 

Research Programʼs rain climatology initiative. This 

section describes the TRMM and Terra products that 

support the monitoring of rainfall and atmospheric 

humidity, including the TRMM ground-truth validation data acquisition.

Precipitation: The majority of global precipitation 

occurs in the tropics, between latitudes of 30 ̊N and 

30 ̊S. Tropical rain is one of the key parameters that 

affects the global heat balance and the global water 

cycle. To understand and predict large variations in 

weather and climate, it is critical to understand the 

coupling between the atmosphere and the surface 

below, which is approximately 29% land and 71% 

ocean. Atmospheric circulation transports both energy and water, moving heat from the tropics to the 

polar regions. Water that has evaporated from the 

oceans and the land surface falls as rain or snow, 

often in places far removed from its point of origin. 

There is evidence that tropical rainfall variability, 

apparently coupled with changes in the underlying 

surface (particularly sea surface temperature), is associated with significant alterations in wind patterns 

and rainfall. Fluctuations in rainfall amount impact 

climate on both short and long time scales.

 Unlike the small-scale general features that appear 

on daily weather maps, the long-term, time-averaged 

circulation and precipitation fields show large-scale 

features associated with the annual evolution of the 

monsoons, trade wind systems, and oceanic convergence zones. Large-scale features are also evident on 

a monthly and seasonal scale. Interannual variability 
Precipitation, Atmospheric Humidity and Atmospheric Temperatures • Overview Data Products Handbook - Volume 1 77

water that the atmosphere can hold without saturation. 

Hence, water vapor amounts decrease from Equator to Pole and with increasing altitude. Generally, 

water amounts are less over the continents than over 

the oceans, and the upper atmosphere is drier than 

the near-surface atmosphere. Superimposed on this 

general behavior are smaller variations of water vapor amounts that determine the formation and properties of clouds and rainfall. Simultaneous imaging 

of clouds and water vapor by TRMM and Terra can 

provide the data necessary to understand better the 

interactions between cloud processes and the largescale flow and distribution of water vapor.

Temperatures: The atmosphere is not physically uniform; it has significant variations in temperature and 

pressure with altitude. The regions where temperature 

is either increasing or decreasing with height define 

the different layers of the atmosphere. The lowest 10 

km of the atmosphere is called the troposphere, and 

the region from about 10 to 50 km is known as the 

stratosphere. The transition region between the two 

is known as the tropopause.

 Atmospheric temperature has a critical influence on 

the hydrological cycle and is a key input parameter in 

global climate models. Tropospheric and stratospheric 

temperatures are central to the problem of greenhouse 

warming. Global Circulation Models (GCMs) predict 

that temperature changes due to higher concentrations of greenhouse gases will have characteristic 

profiles in the different layers of the atmosphere, 

with warming in the mid-troposphere and cooling in 

the stratosphere. Cooler stratospheric temperatures 

are an expected consequence of the increased trapping 

of terrestrial radiation in the troposphere. In addition, 

most ozone depletion occurs in the lower part of the 

stratosphere (15 to 25 km) so an accurate knowledge 

of the fluctuations in stratospheric temperature on 

a global scale is therefore required for an overall 

understanding of global warming.

 The most important data required for initializing 

forecast models are the 3-D horizontal wind and 

temperature profiles. Other desirable parameters are 

the 3-D moisture distribution, surface pressure and 

wind fields, and sea-surface temperature. Problems 

exist in the establishment of a 3-D global temperature 

field because of data gaps over the ocean. In addition, 

sharp temperature inversions exist in the atmosphere, 

which are difficult to infer from satellite.

Product Overview

The products included in this section are primarily 

produced by the TRMM instruments, with MODIS 

on the Terra satellite providing two products giving 

humidity data.

 The TRMM mission is designed to use the measurements of multiple instruments flying on its platform to quantify the distribution of the two-thirds of 

global precipitation estimated to fall in the tropics 

and subtropics. PR measures the three-dimensional 

rainfall distribution over both land and ocean. More 

specifically, this instrument provides information 

about the vertical structure of precipitation, the key 

to determining the latent heat input to the atmosphere. 

A unique feature is that it permits the measurement of 

rain over land where passive microwave precipitation 

estimation techniques are difficult.

 The fundamental data set obtained from the TRMM 

program is a three-year sequence of instantaneous 

rainfall rates as well as monthly, tropical rainfall accumulations averaged over geographical grid squares 

500 km on a side. These gridded monthly averages 

are being used to validate climate forecasting models. 

The TRMM data product sequence begins with the 

TMI data and the precipitation radar data, which form 

the basis of the improved rainfall estimation promised 

by the mission. Rainfall estimates from TMI and the 

PR are combined in TRMM product 3B-31, and then 

combined with Sun-synchronous low-orbit satellite 

microwave data (SSM/I), geostationary satellite IR 

data (e.g., GOES) and rain gauge data to produce the 

final rainfall product (3B-43).

 The MODIS products provide water vapor measurements (MOD05) needed for atmospheric correction algorithms for all surface parameter retrieval 

tasks. They are also used to improve understanding of 

the hydrological cycle, energy budget, and climate. 

The atmospheric stability parameter in MOD07 gives 

information on convection and instability which, 

along with temperature and moisture profiles, also 

contained in product MOD07, is needed by the atmospheric correction algorithms.

 A set of TRMM ground validation products from 

approximately ten independent sites is included. 

These products provide the rainfall data used to validate the data obtained from PR on TRMM. Included 

are the instantaneous surface rainfall maps (2A-53), 

the radar site Convective/Stratiform Map (2A-54), 

and the radar site gridded reflectivities (2A-55) used 

to validate the vertical hydrometeor and latent-heating retrievals of TMI and PR. Five-day and 30-day 

rainfall data at the radar sites (3A-53, 54) and monthly 

3-D radar reflectivity distributions (3A-55) complete 

the set of products that are needed to validate and 

improve all the TRMM rainfall products.
78 Data Products Handbook - Volume 1 Overview • Precipitation, Atmospheric Humidity and Atmospheric Temperatures 

Product Interdependency and 

Continuity with Heritage Data

TRMM carries the first quantitative precipitation 

radar to be flown in space. Previous measurements 

from space of rain rate have used passive microwave 

measurements from instruments on Nimbus and the 

Defense Meteorological Satellites, which are in polar orbits and cannot capture the diurnal variability 

of rainfall in the tropics. TMI on TRMM provides 

continuity with these earlier data, which are used 

in conjunction with PR and the heritage instrument 

data to provide greatly-improved accuracy in rainfall mapping. The radiometers on TRMM give good 

measurements of rainfall rates over the oceans, but 

such measurements continue to be less reliable over 

land where the surface emissivity inhomogeneities 

make interpretation more difficult. VIRS, which was 

derived from AVHRR heritage technology, is also 

included in the TRMM instrument complement so 

that the rainfall measurements from the radar and 

passive microwave instruments can be used to better 

interpret VIS/IR measurements from past and future 

operational satellites.

 The MODIS humidity products are being used by 

atmospheric characterization and climate modeling 

researchers in conjunction with the rainfall data, even 

though there is not a direct product dependence between MODIS and TRMM.

 The present series of atmospheric sounders (TOVS) 

aboard NOAA polar satellites are limited by their 

vertical resolution. NOAA and the Defense Meteorological Satellite Program (DMSP) operational satellites have provided temperature and humidity profiles and surface products for ingest into assimilation 

models. Three important NOAA instruments are: 

AVHRR since 1981; TOVS since 1979; the Geostationary Operational Environmental Satellite (GOES) 

Visible Infrared Spin-Scan Radiometer (VISSR) and 

its VISSR atmospheric sounder version since 1978. 

Considerable progress was also made with the deployment of the three-axis stabilized high-resolution 

infrared sounder on geostationary satellites. Lastly, 

the 20-channel Advanced Microwave Sounding Unit 

(AMSU) on NOAA-K, provides higher-resolution 

temperature profiles than its predecessor, the fourchannel MSU.

Suggested Reading

Chiu, L. S., 1988.

Inoue, T., 1997.

Kummerow, C. et al., 1998.

Kummerow, C. et al., 2000.

Li, J., and K. Nakamura, 2002.

Liao, L. et al., 2001.

Pfaendtner, J. et al., 1995.

Rasmusson, E. M., 1985.

Schubert, S. D. et al., 1993.

Simpson, J. et al., 1996.

Takacs, L. L. et al., 1994.

Tao, W.-K. et al., 2001.
Precipitation, Atmospheric Humidity and Atmospheric Temperatures • Overview Data Products Handbook - Volume 1 79

PR Surface Cross-Section as 

Function of Scan Angle 

(PR 2A-21)

Product Description

This Level 2 product provides an estimate of the path 

attenuation and its reliability by using the surface as a 

reference target. Path attenuation is estimated by comparing the apparent surface cross section measured in 

the presence of rain, with the averaged surface cross 

section measured in the absence of rain. A reliability 

parameter is also provided based on the variability of 

the surface cross section in the absence of rain. The 

product includes the spatial and temporal statistics 

of the surface scattering cross section, and classification of the cross sections into land/ocean, rain/no rain 

categories. Output parameters consist of attenuation, 

reliability, and surface cross section (σ0).

Research and Applications

One of the ways by which attenuating-wavelength 

radars such as TRMM can correct for attenuation 

is by means of the surface reference method. In this 

method, the path-integrated attenuation caused by 

rain is estimated by a ratio of the apparent surface 

cross section, σ0, in the raining region to a temporally or spatially averaged value of this quantity in 

rain-free regions.

 As the surface cross sections are closely related 

to the vegetation and soil moisture conditions over 

land, and the wind speed and directions over oceans, 

a number of TRMM-related studies can be undertaken 

with these data sets.

Data Set Evolution

There have been limited σ0 measurements made from 

aircraft radars, but systematic global maps had not 

previously been compiled.

Relation to Other TRMM Products

This product is used as input for the PR rain profiling 

algorithm (2A-25). For areas far away from rainfall, 

σ0 is a unique product of TRMM.

Suggested Reading

Caylor, I. J. et al., 1997.

Iguchi, T., and R. Meneghini, 1994.

Kozu, T., 1995.

Marzoug, M., and P. Amayenc, 1994.

Meneghini, R., and K. Nakamura, 1990.

Meneghini, R., and T. Kozu, 1990.

Meneghini, R. et al., 2000.

PR Surface Cross-Section as Function of 

Scan Angle Product Summary

Coverage: 36°S to 36°N; 15-16 orbits per day 

with a swath width of 220 km before August 

2001 and 253 km after August 2001 (satellite 

altitude was raised from 350 km to 402.5 km)

Spatial/Temporal Characteristics: 5 km (after 

August 2001)

Key Geophysical Parameters: Rainfall profile

Processing Level: 2

Product Type: Standard, at-launch

Maximum File Size: 12 MB

File Frequency: 1/orbit

Primary Data Format: HDF

Browse Available: Yes

Additional Product Information: 

trmm.gsfc.nasa.gov

tsdis.gsfc.nasa.gov

DAAC: Goddard Space Flight Center Earth 

Sciences DAAC, daac.gsfc.nasa.gov

Science Team Contact: K. Okamoto

TRMM / PR
80 Data Products Handbook - Volume 1 • Precipitation, Atmospheric Humidity and Atmospheric Temperatures 

PR Rain Occurrence and Rain 

Type and Bright Band Height

(PR 2A-23)

Product Description

The Level 2 product 2A-23 indicates the presence of 

a “bright band,” which in turn indicates the presence 

of stratiform rain. If the bright band is detected, its 

height is determined. This information is used to classify the rain type (stratiform type, convective type, 

or warm rain). The TRMM Science Data and Information System (TSDIS) supplies information as to the 

background type: land, ocean, or indeterminate. The 

height of the bright band is useful to the TMI algorithms to identify the height of the freezing level.

Research and Applications

PR provides information on the distribution and 

characterization of rainfall and latent heating over 

the global tropics and subtropics. This information 

is used to advance the Earth system science objective 

to understand global energy and water cycles. In addition, it provides understanding of the mechanism 

through which tropical rainfall and its variability influence global atmospheric circulation by improving 

the modeling of rainfall and atmospheric circulation 

interactions. This enables the improved prediction 

of monthly and longer global circulation and rainfall 

variability. Finally, it improves modeling of convectively-driven precipitating systems in the tropics, including their organization on the mesoscale and their 

interactions with the ocean and the atmosphere.

Data Set Evolution

There have been a number of airborne radars that have 

flown on field campaigns in previous years. Most 

notably, ARMAR is a 13.8 GHz radar that flew on 

the NASA DC-8 and was built specifically to simulate 

TRMM observations.

Relationship to Other TRMM Products

Product 2A-23 is a qualitative description not previously available. It is useful to passive microwave 

determinations as well as input to the PR rain algorithm.

Suggested Reading

Awaka, J. et al., 1998.

Furuhama, Y. et al., 1982.

Iguchi, T., and R. Meneghini, 1994.

Steiner, M. et al., 1995.

Theon, J. S. et al., Eds., 1992.

PR Rain Occurrence and Rain Type and 

Bright Band Height Product Summary

Coverage: 36°S to 36°N; 15-16 orbits per day 

with a swath width of 220 km before August 

2001 and 253 km after August 2001 (satellite 

altitude was raised from 350 km to 402.5 km)

Spatial/Temporal Characteristics: 5 km (after 

August 2001)

Key Geophysical Parameters: Rainfall profile

Processing Level: 2

Product Type: Standard, at-launch

Maximum File Size: 15 MB

File Frequency: 1/orbit

Primary Data Format: HDF

Browse Available: Yes

Additional Product Information: 

trmm.gsfc.nasa.gov

tsdis.gsfc.nasa.gov

DAAC: Goddard Space Flight Center Earth Sciences DAAC, daac.gsfc.nasa.gov

Science Team Contact: K. Okamoto

TRMM / PR
Precipitation, Atmospheric Humidity and Atmospheric Temperatures • TRMM / PR Data Products Handbook - Volume 1 81

PR Range Profiles of Rain and 

Water Content (PR 2A-25)

Product Description

Level 2 product 2A-25 retrieves rain parameters 

over each PR resolution cell (4 km × 4 km × 250 m). 

Products 1C-21, 2A-21, and 2A-23 are input to this 

product. In the presence of rain, these parameters 

include rainfall structure, estimated path attenuation, 

and its reliability.

Research and Applications

PR provides information on the distribution and 

characterization of rainfall and latent heating over 

the global tropics and subtropics. This information 

is used to advance the Earth system science objective 

to understand global energy and water cycles. In addition, it provides understanding of the mechanism 

through which tropical rainfall and its variability influence global atmospheric circulation by improving 

the modeling of rainfall and atmospheric circulation 

iterations. This enables the improved prediction of 

monthly and longer global circulation and rainfall 

variability. Finally, it improves modeling of convectively-driven precipitating systems in the tropics, including their organization on the mesocale and their 

interactions with the ocean and the atmosphere.

Data Set Evolution

There have been a number of airborne radars that have 

flown on field campaigns in previous years. Most 

notably, ARMAR is a 13.8 GHz radar that flew on 

the NASA DC-8 and was built specifically to simulate 

TRMM observations.

Relationship to Other TRMM Products

Product 2A-25 provides the best single-instrument 

rain profile information. Over the narrow swath of 

the radar it is used to improve the passive microwave 

algorithm (2A-12), thereby extending the usefulness 

of PR to the wide swath of TMI.

Suggested Reading

Iguchi, T. et al., 1998.

Iguchi, T., and R. Meneghini, 1994.

Iguchi, T. et al., 2000.

Kozu, T. et al., 1991.

Kozu, T., and T. Iguchi, 1996.

Theon, J. S. et al., Eds., 1992.

PR Range Profiles of Rain and Water 

Content Product Summary

Coverage: 36°S to 36°N; 15-16 orbits per day 

with a swath width of 220 km before August 

2001 and 253 km after August 2001 (satellite 

altitude was raised from 350 km to 402.5 km)

Spatial/Temporal Characteristics: 5 km (after 

August 2001)

Key Geophysical Parameters: Rainfall profile

Processing Level: 2

Product Type: Standard, at-launch

Maximum File Size: 260 MB

File Frequency: 1/orbit

Primary Data Format: HDF

Browse Available: Yes

Additional Product Information: 

trmm.gsfc.nasa.gov

tsdis.gsfc.nasa.gov

DAAC: Goddard Space Flight Center Earth 

Sciences DAAC, daac.gsfc.nasa.gov

Science Team Contact: K. Okamoto
82 Data Products Handbook - Volume 1 • Precipitation, Atmospheric Humidity and Atmospheric Temperatures 

PR Monthly Accumulated Rainfall and Vertical Structure (PR 

3A-25) and Monthly

Combined Accumulated

Surface Rainfall (PR 3A-26)

Product Description

Statistics of rain data are provided in this Level 3 

product, including probability distributions of rain 

rates, to help identify and characterize sampling 

errors, and to determine patterns in precipitation 

movement and latent heat transfer. The statistical 

nature of the rainfall is characterized by the outputs 

from the Level 2 radar algorithms. Four categories of 

output products are computed over 5° × 5° × 1 month 

domains. The four categories are: 

1)mean and variance of the rain rate at five heights 

above the surface; 

2)fractional rain areas (stratiform, convective, 

both); 

3)histograms of the rain rates, reflectivity factors, 

heights of the bright-band and storm top, and path 

attenuation; and 

4)correlation coefficients between rain rates at the 

various heights.

Research and Applications

This product is designed to provide the temporal 

continuity and spatial coverage necessary for routine 

evaluation of time-space mean precipitation. Applications include quantitative estimates of large-scale, 

time-averaged precipitation over the global tropics, 

analyses of the seasonal, interannual, and diurnal variability of precipitation, and the establishment of multiyear time series data for global climate change.

Relationship to Other TRMM Products

Inputs to algorithm 3A-25 are the output products 

from the Level 2 PR algorithms. A few of the output 

products from 3A-25 and 3A-26 provide estimates 

of the same quantity, e.g., the monthly mean rain rate 

at the 5° × 5° boxes over the TRMM domain. The 

approach used in 3A-25 is that of a straightforward 

sample mean of the individual measurements obtained 

during the month whereas 3A-26 uses a somewhat 

more involved statistical approach. Comparisons of 

these products over the TRMM ground-validation 

sites are obviously important. As the TMI 3A-11 

algorithm outputs monthly means over the same 5° 

× 5° cells as does 3A-25 and 3A-26, comparisons of 

the output products should be made. To reduce the 

effects of sampling errors on 3A-25 and 3A-26, comparisons of these outputs with those of 3A-11 should 

also be made over longer time periods (3 months, 6 

months, and yearly).

Data Set Evolution

Approaches for measuring statistically averaged rain 

rates for TRMM have their heritage in the GARP 

Atlantic Tropical Experiment (GATE) measured rain 

rates experiments, as well as the Florida Area Cumulus Experiment (FACE). The GATE and FACE radar rain distributions were the heritage data sets used 

to describe some of the key statistics characteristics of 

the precipitation that TRMM is intended to measure. 

Additionally, by identifying the ranges of rain rates 

measured in these experiments, they served to enable 

instrument designers to better specify the parameters 

and performance of the observing system.

Suggested Reading

Atlas, D. et al., 1990.

Kedem, B. et al., 1990.

Meneghini, R., 1998.

Short, D. et al., 1993.

Meneghini, R. et al., 2001.

Meneghini, R., and J. A. Jones, 1993.

Wilheit, T. T. et al., 1991.

TRMM / PR
Precipitation, Atmospheric Humidity and Atmospheric Temperatures • Data Products Handbook - Volume 1 83

PR Monthly Accumulated Rainfall and 

Vertical Structure and Monthly Combined 

Accumulated Surface Rainfall Product 

Summary

Coverage: 40°N to 40°S

Spatial/Temporal Characteristics: 5° × 5° and 

0.5° × 0.5°, monthly

Key Geophysical Parameters: 

Low Resolution – mean and standard deviation 

of rain rate, storm height, xi, brightband height 

and NWBF correction, rain fraction, histograms 

of above, correction coefficients

High Resolution – mean and standard deviation of rain rate, rain fraction

Processing Level: 3

Product Type: Standard, at-launch

Maximum File Size: 30 MB (3A-25), 

6 MB (3A-26) 

File Frequency: 1/month

Primary Data Format: HDF

Browse Available: Yes

Additional Product Information: 

trmm.gsfc.nasa.gov

tsdis.gsfc.nasa.gov

DAAC: Goddard Space Flight Center Earth Sciences DAAC, daac.gsfc.nasa.gov

Science Team Contact: K. Okamoto

TRMM / PR and TMI

TMI Surface Rainfall and 

Vertical Structure (TMI 2A-12)

Product Description

This product contains vertical hydrometeor profiles 

from the TMI obtained by blending the radiometric 

data with dynamical cloud model data. The results 

consist of the surface rainfall rate and a confidence 

parameter, as well as three-dimensional results, with 

five hydrometeor classes and the associated latent 

heating derived at approximately 14 vertical layers. 

The layer information must be viewed as approximate 

since it is inferred through the cloud model rather than 

from direct measurements. This is a Level 2 product 

that has not been resampled; scan time, latitude, longitude, and scan line/pixel number are included for 

each pixel in this product.

Research and Applications

The research objectives of this product are to:

• improve the ability to obtain rainfall and latent heat 

release over the global tropics;

• validate and improve models that promote the 

understanding of the formation and organization 

of convection and their interactions with the ocean 

and ambient atmosphere and the climate system; 

and 

• advance understanding of the Earthʼs global energy and water cycles by determining how tropical 

rainfall and its variability influence global circulation.

Relationship to other TRMM products

This product generates output similar to 2A-25 

and 2B-31. Product 2A-25, the PR rainfall vertical 

structure has better vertical resolution (250 m), but is 

limited to a relatively narrow swath of approximately 

250 km as compared to the approximately 870 km 

swath of the TMI (after the altitude of the satellite 

was raised from 350 km to 402.5 km in August 2001). 

Sensitivity to the integrated ice content is also greater 

from the TMI product than the PR product.

 Product 2B-31 combines information from both the 

radar and radiometer over the narrow swath of the 

radar. In this region, 2B-31 should ultimately provide 

the best surface rainfall and structure products. Problems related to co-registration of the sensors, as well 

as the lack of PR heritage, made this product (2B-31) 

less reliable at the onset of the mission.
84 Data Products Handbook - Volume 1 TRMM / TMI • Precipitation, Atmospheric Humidity and Atmospheric Temperatures 

Data Set Evolution and Applications

The TMI is similar to several spaceborne microwave 

instruments that have flown over the past decade, 

including the SSM/I onboard the DMSP satellites and 

the SMMR onboard Nimbus-7. The major changes 

are the addition of 10 GHz dual-polarized channels 

and increased spatial resolution, which reduce the 

footprint filling uncertainty that affects passive microwave algorithms.

Suggested Reading

Hong, Y. et al., 1999.

Kummerow, C. et al., 1998.

Kummerow, C. et al., 1996.

Olson, W. et al., 1999.

Kummerow, C., and L. Giglio, 1994.

Kummerow, C. et al., 2001.

Mugnai, A. et al., 1993.

Smith, E. A. et al., 1994a,b.

TMI Surface Rainfall and Vertical Structure 

Product Summary

Coverage: 38°S to 38°N; 15-16 orbits per day 

with a swath width of 760 km before August 

2001 and 874 km after August 2001 (satellite 

altitude was raised from 350 km to 402.5 km).

Spatial/Temporal Characteristics: 5 km

Key Geophysical Parameters: Surface rainfall

Processing Level: 2

Product Type: Standard, at-launch

Maximum File Size: 105 MB

File Frequency: 1/orbit

Primary Data Format: HDF

Browse Available: Yes

Additional Product Information: 

trmm.gsfc.nasa.gov

tsdis.gsfc.nasa.gov

DAAC: Goddard Space Flight Center Earth 

Sciences DAAC, daac.gsfc.nasa.gov

Science Team Contact: C. Kummerow

TMI Monthly Surface Rainfall

(TMI 3A-11)

Product Description

This product consists of mean monthly rainfalls over 

5° × 5° cells. Retrievals are only possible over oceanic 

areas, where data are needed most for climate model 

verification. The frequency of occurrence of rain intensities in different rate categories over a specified 

area can be plotted as a histogram or as a smoothed 

curve that fits the histogram. This curve is called the 

probability density function (pdf) of the rain rates. 

In the case of tropical rainfall, the pdfs are always 

highly skewed such that a large fraction of the total 

rainfall is concentrated into a relatively small number 

of storms of high intensity or in a small fraction of the 

time or of the area in which it is raining. This product 

is obtained by considering histograms of 19-GHz, 21-

GHz and 19-21-GHz combinations. Monthly rainfall 

indices are computed by statistically matching the 

histograms with the model calculated pdfs. The main 

purpose of this product is to provide a robust baseline 

of surface rainfall rates.

Research and Applications

This product is designed to advance understanding 

of the Earthʼs global energy and water cycles by 

determining how tropical rainfall and its variability 

influence global circulation. A reliable measurement 

of rainfall, particularly over the oceans, provides for 

the first time space-time variability of vertically integrated latent heating. This, coupled with information 

on the nature of synoptic disturbances in different 

regions of the tropics, can help determine the vertical 

structure of the diabatic heating.

Data Set Evolution

The TMI is similar to several spaceborne microwave 

instruments that have flown over the past decade including the SSM/I onboard the DMSP satellites and 

the SMMR onboard Nimbus-7. The major changes 

are the addition of 10-GHz polarized channels 

and increased spatial resolution, which reduce the 

footprint filling uncertainty that affects passive microwave algorithms. The surface rainfall algorithm 

is very similar to the one developed and applied as 

part of the Global Precipitation Climatology Project 

(GPCP) program for the past eight years.
Precipitation, Atmospheric Humidity and Atmospheric Temperatures • Data Products Handbook - Volume 1 85

Relationship to Other TRMM Products

This product generates monthly surface rainfall with 

sampling corresponding to the wide swath of the TMI. 

This parameter may also be obtained from the TRMM 

combined sensor algorithm but is included as a standard product to serve as a very robust baseline.

Suggested Reading

Chang, A. T. C. et al., 1999a,b.

Chiu, Long S. et al., 1993.

Wilheit, T. T. et al., 1991.

TMI Monthly Surface Rainfall Product 

Summary

Coverage: Day and night, 40°N to 40°S over 

oceans only

Spatial/Temporal Characteristics: 5° × 5°, 

monthly

Key Geophysical Parameters: Surface rainfall 

and statistics

Processing Level: 3

Product Type: Standard, at-launch

Maximum File Size: 60 MB

File Frequency: 1/month

Primary Data Format: HDF

Browse Available: Yes

Additional Product Information: 

trmm.gsfc.nasa.gov

tsdis.gsfc.nasa.gov

DAAC: Goddard Space Flight Center Earth 

Sciences DAAC, daac.gsfc.nasa.gov

Science Team Contact: A. Chang

TRMM / TMI

TRMM Combined Surface 

Rainfall Rate and Vertical 

Structure (PR 2B-31)

Product Description

This is a Level 2 product, which combines information from more than one sensor. This product 

contains vertical hydrometeor profiles obtained by 

blending reflectivities obtained by PR with brightness 

temperatures from the TMI instrument. The results 

consist of the surface rainfall rate and a confidence 

parameter. Also included are the three-dimensional 

results, with five hydrometeor classes and the associated latent heating derived at the vertical resolution 

of the PR (250 m).

 This product is designed to improve our ability to 

obtain rainfall and latent heat release over the global 

tropics. Additionally, it is used to validate and improve models that promote the understanding of the 

formation and organization of convection and their 

interactions with the ocean and ambient atmosphere 

and the climate system. Finally, it advances our understanding of the Earthʼs global energy and water 

cycles by determining how tropical rainfall and its 

variability influence global circulation.

Research and Applications

The vertical distribution of hydrometeors is an important part of any surface rainfall rate retrieval algorithm, because the distribution strongly affects the 

microwave radiances. The intent is that this product 

should constitute the best product available from the 

TRMM mission.

Relationship to Other TRMM Products

This product generates output similar to 2A-12 and 

2A-25. Product 2A-12 (the TMI rainfall vertical structure) has wider coverage (approximately 870 km vs. 

250 km, after the alititude of the satellite was raised 

from 350 km to 402.5 km in August 2001).

Data Set Evolution and Applications

The simultaneous use of active and passive microwave sensors can yield dramatic improvements 

over either sensor alone. Some limited aircraft studies have demonstrated this point. The opportunity to 

merge radiances from active and passive sensors, as 
86 Data Products Handbook - Volume 1 TRMM • Precipitation, Atmospheric Humidity and Atmospheric Temperatures 

well as visible and infrared radiances on a global 

data set, is a capability that is unique to the TRMM 

satellite.

Suggested Reading

Haddad, Z. et al., 1997a,b.

Schols, J. L., and J. A. Weinman, 1994.

Smith, E. A. et al., 1994b.

Smith, E. A. et al., 1997.

Smith, E. A. et al., 2003.

TRMM Combined Surface Rainfall Rate 

and Vertical Structure Product Summary

Coverage: 36°N to 36°S; 15-16 orbits per 

day with a swath width of 220 km before 

August 2001 and 253 km after August 2001 

(satellite altitude was raised from 350 km to 

402.5 km).

Spatial/Temporal Characteristics: 5 km (after 

August 2001)

Key Geophysical Parameters: Rainfall profile

Processing Level: 2

Product Type: Standard, at-launch

Maximum File Size: 170 MB

File Frequency: 1/orbit

Primary Data Format: HDF

Browse Available: Yes

Additional Product Information: 

trmm.gsfc.nasa.gov

tsdis.gsfc.nasa.gov

DAAC: Goddard Space Flight Center Earth 

Sciences DAAC, daac.gsfc.nasa.gov

Science Team Contact: E. Smith

TRMM Monthly Combined Accumulated Rainfall and 

Vertical Structure (3B-31)

Product Description

This Level 3 product consists of rainfall accumulations over 5° × 5° areas and monthly time scales 

of surface rainfall, as well as the three-dimensional 

hydrometeor structure and measurements of latent 

heating. Retrievals are made over both land and 

ocean using the sampling characteristics of the TMI 

instrument. The product is a calibration of the TMI 

profiling algorithm (2A-12), where the calibration 

is obtained by comparing the coincident geophysical products from the TMI 2A-12 with those of the 

combined sensors approach (2B-31). The calibration 

coefficients are then applied to the entire 2A-12 data 

set and monthly accumulations are computed.

 The simultaneous use of active and passive microwave sensors can yield dramatic improvements 

over either sensor alone, as demonstrated by earlier 

aircraft studies. The opportunity to merge radiances 

from active and passive sensors as well as visible and 

infrared radiances on a global data set is a capability 

that is unique to the TRMM satellite. As the algorithm 

has matured, this product has formed the cornerstone 

of TRMM retrievals and serves as the transfer standard to calibrate previous satellite measurements.

Research and Applications

Before the launch of TRMM there were no reliable 

data sets for either monthly or seasonal mean rainfall or for their interannual variability to validate the 

global atmospheric models. Latent heat derived from 

precipitation is the major source of energy within the 

atmosphere, and its vertical distribution affects the 

stability of the atmosphere and hence the propagation 

pattern of Rossby waves, which influence the climate 

in regions that are located far from the tropical storms 

where the latent energy is released. This product allows latent heating to be calculated from condensation. The direct observation of precipitation, and 

the cloud systems that produce it in varying amounts, 

permits researchers to know exactly where the latent 

heat is being released. This information can be used as 

input to climate models to determine higher latitude 

effects and possible interannual changes.
Precipitation, Atmospheric Humidity and Atmospheric Temperatures • TRMM Data Products Handbook - Volume 1 87

Relationship to Other TRMM Products

This product generates monthly surface rainfall and 

latent heating with sampling corresponding to the 

wide swath of the TMI. Product 3A-25 generates 

similar products but only uses the narrower swath 

of the PR.

Data Set Evolution

Precipitation distributions have already been retrieved 

from spaceborne microwave radiometers, and TMI 

builds on that earlier work. The primary instrument 

currently used to retrieve volumetric rainfall from 

space is the SSM/I passive radiometer system on the 

DMSP. The TRMM Combined Monthly Rainfall 

product overcomes the limitation of radiometric measurements, which include difficulty in measuring light 

rain and snowfall over land. Combining radar and 

radiometric measurements in a common precipitation 

retrieval algorithm takes advantage of the strengths 

of both types of measurements.

Suggested Reading

Smith, E. A. et al., 1994b.

Smith, E. A. et al., 1997.

Smith, E. A. et al., 2003.

Schols, J. L., and J. A. Weinman, 1994.

TRMM Monthly Combined Accumulated 

Rainfall and Vertical Structure Product 

Summary

Coverage: Day and night, 40°N to 40°S

Spatial/Temporal Characteristics: 5° × 5°,

14 levels, monthly

Key Geophysical Parameters: Surface rainfall 

and hydrometeor profiles

Processing Level: 3

Product Type: Standard, at-launch

Maximum File Size: 500 KB

File Frequency: 1/month

Primary Data Format: HDF

Browse Available: Yes

Additional Product Information: 

trmm.gsfc.nasa.gov

tsdis.gsfc.nasa.gov

DAAC: Goddard Space Flight Center Earth 

Sciences DAAC, daac.gsfc.nasa.gov

Science Team Contact: E. Smith
88 Data Products Handbook - Volume 1 TRMM • Precipitation, Atmospheric Humidity and Atmospheric Temperatures 

TRMM 1° Daily Combined 

Rainfall (TRMM 3B-42) and 

Monthly Combined Instrument 

Rainfall (TRMM 3B-43)

Product Description

This pair of products implements the “flying rain 

gauge” aspect of TRMM by calibrating other precipitation estimates with the “best” TRMM estimate 

of precipitation to provide improved rainfall estimates 

on finer scales than TRMM can provide by itself. The 

TRMM 1° Daily Combined Rainfall (3B-42) product 

uses TRMM estimates of surface rain and hydrometeor structure to calibrate and adjust rain estimates 

made from geosynchronous infrared observations. 

The Monthly Combined Instrument Rainfall (3B-43) 

product combines 3B-42 with rain gauge analyses 

(where available) to objectively determine the best 

estimate of rainfall on the space and time scales indicated. This includes adjustments for bias between 

the satellite and gauge fields, where feasible, and 

objective combination based on the random errors 

estimated for each field. Each product has an associated field of estimated random error that reflects the 

local sampling of the various input fields and the 

reliability of each data source.

Research and Applications

Surface rainfall in many parts of the world is very 

poorly documented on the time and space scales 

that these products provide. These products provide 

considerably more detail for validation of numerical 

weather and climate predictions, accumulations into 

seasonal-scale climatologies, and input to surface 

hydrology models. Also, these products provide systematic insight into the accuracy of the independent 

input fields, both regionally and seasonally, which 

motivates fresh research to improve the precipitation 

estimation process.

Relationship to Other TRMM Products

The Surface Rainfall and Vertical Structure (2B-31), 

and consequently all of the products antecedent to 

2B-31, is key to making 3B-42 (and hence 3B-43) 

as accurate as possible. The TRMM VIRS 11-μm 

temperatures (1B-01) provide key data in the calibration step of 3B-42. The TRMM Daily and Monthly 

Rainfall products provide finer space and time scales 

than pure TRMM products, but at the expense of depending on data sources outside TRMM.

Data Set Evolution

The analysis procedures for 3B-42 and 3B-43 were 

developed for rainfall estimates from the SSM/I, 

which has flown on selected Defense Meteorological 

Satellite Program (DMSP) polar-orbiting platforms 

since mid-1987. The TRMM versions of both procedures are third-generation code; further development 

is expected as researchers gain experience with the 

new data sources.

Suggested Reading

Adler, R. F. et al., 1994.

Adler, R. F. et al., 2000.

Huffman, G. J. et al., 1995.

Huffman, G. J. et al., 1997a,b.

TRMM 1° Daily Combined Rainfall and 

Monthly Combined Instrument Rainfall 

Product Summary

Coverage: Day and night, 40°N to 40°S

Spatial/Temporal Characteristics: 1° × 1°; 

daily (3B-42), monthly (3B-43)

Key Geophysical Parameters: Surface rainfall

Processing Level: 3

Product Type: Standard, at-launch

Maximum File Size: 150 KB (3B-42), 250 KB 

(3B-43)

File Frequency: 1/day (3B-42), 1/month (3B43)

Primary Data Format: HDF

Browse Available: Yes

Additional Product Information: 

trmm.gsfc.nasa.gov

tsdis.gsfc.nasa.gov

DAAC: Goddard Space Flight Center Earth 

Sciences DAAC, daac.gsfc.nasa.gov

Science Team Contact: R. Adler
Precipitation, Atmospheric Humidity and Atmospheric Temperatures • TRMM Data Products Handbook - Volume 1 89

TRMM Instantaneous Radar 

Site Rain Map (TRMM 2A-53)

Product Description

The Level 2A ground validation Radar Site Rain 

Map (2A-53) product contains instantaneous (highest temporal, 2-km horizontal resolution) rainmaps 

produced from the 1C-51 reflectivity fields. These 

require application of an appropriate reflectivity (Z) 

to rain (R) transformation, determined separately 

for each site on the basis of rain gauge, radar, and 

raindrop size information.

Research and Applications

This product is used to determine the intensity, duration, and spatial extent of rain-producing systems 

at each ground validation site.

Relationship to Other TRMM Products

This product is necessary to validate instantaneous 

rainfall retrievals derived from measurements by 

instruments on the TRMM satellite (2A-12, 2A-25, 

2B-31).

Data Set Evolution

Rainfall estimations from surface-based radar observations have been used operationally for several decades in short-term forecasting and for hydrological 

purposes. Recent upgrades to the operational systems 

and rainfall algorithms are used in the TRMM Ground 

Validation Program.

Suggested Reading

Krajewski, W. F., 1987.

Rosenfeld, D. et al., 1994.

Steiner, M. et al., 1995.

TRMM Instantaneous Radar Site Rain Map 

Product Summary

Coverage: 150-km radius of ground validation 

radar, continuous

Spatial/Temporal Characteristics: 2 km (minimum)

Key Geophysical Parameters: Surface rainfall

Processing Level: 2A

Product Type: Validation, at-launch

Maximum File Size: 2 MB

File Frequency: 1/hour

Primary Data Format: HDF

Browse Available: Yes

Additional Product Information: 

trmm.gsfc.nasa.gov

tsdis.gsfc.nasa.gov

DAAC: Goddard Space Flight Center Earth 

Sciences DAAC, daac.gsfc.nasa.gov

Science Team Contact: R. Houze
90 Data Products Handbook - Volume 1 TRMM • Precipitation, Atmospheric Humidity and Atmospheric Temperatures 

TRMM Instantaneous Radar 

Site Convective/Stratiform Map 

(TRMM 2A-54)

Product Description

The Level 2A ground validation Radar Site 

Convective/Stratiform Map identifies rainfall from 

TRMM 2A-53 as occurring from either convectiveor stratiform-type clouds and generated from the 

reflectivity product 1C-51.

Research and Applications

Characterization of precipitation types as stratiform 

or convective is essential for determining the vertical 

profile of heating associated with these two major 

classifications. Classification methods include information on the vertical and horizontal structure of 

reflectivity fields.

Relationship to Other TRMM Products

Product 2A-54 is used to validate the convective/

stratiform separation derived by the PR algorithm 

2A-23. The convective stratiform classification map 

performs as input to 3A-55 to be used in evaluation 

of the vertical heating profile, which is of interest to 

large-scale models for initialization and validation 

studies.

Data Set Evolution

Convective/stratiform classification methods have 

been developed for use in studies of the structure, 

dynamics, and evolution of tropical mesoscale convective systems.

Suggested Reading

Churchill, D. D., and R. A. Houze, Jr., 1984.

Houze, R. A. Jr., 1989.

Rosenfeld, D. et al., 1995.

Steiner, M. et al., 1995.

TRMM Instantaneous Radar Site 

Convective/Stratiform Map Product 

Summary

Coverage: 150-km radius of ground validation 

radar, continuous

Spatial/Temporal Characteristics: 2 km (minimum)

Key Geophysical Parameters: Surface rainfall

Processing Level: 2A

Product Type: Validation, at-launch

Maximum File Size: 1 MB

File Frequency: 1/hour

Primary Data Format: HDF

Browse Available: Yes

Additional Product Information: 

trmm.gsfc.nasa.gov

tsdis.gsfc.nasa.gov

DAAC: Goddard Space Flight Center Earth Sciences DAAC, daac.gsfc.nasa.gov

Science Team Contact: R. Houze
Precipitation, Atmospheric Humidity and Atmospheric Temperatures • TRMM Data Products Handbook - Volume 1 91

TRMM Instantaneous Radar 

Site 3-D Reflectivities

(TRMM 2A-55)

Product Description

The Level 2A ground validation Radar Site 3-D 

Reflectivity (2A-55) product contains instantaneous 

(highest temporal, 2-km horizontal resolution) 3-D 

Cartesian-gridded reflectivities, interpolated from 

volume-scan reflectivities (1C-51). The origin and 

outer boundaries of the 3-D Cartesian grid are determined by the TRMM Ground Validation Team. 

In addition to the 3-D gridded reflectivity data, this 

product includes vertical profiles of the mean reflectivity and the frequency distribution at each height. 

The frequency distribution is provided in the form of 

contoured frequency by altitude diagrams (CFADs). 

Vertical profiles and CFADs are provided for the 

following categories: total, total-land, total-ocean, 

total-convective, total-stratiform, total-anvil, landconvective, land-stratiform, land-anvil, ocean-convective, ocean-stratiform, and ocean-anvil. 

Research and Applications

The 3-D structure of radar reflectivity is determined 

by hydrometeor distributions within cloud systems 

and is indicative of both microphysical and dynamical 

processes within such systems. The reflectivity structure can be used to simulate satellite observations of 

precipitating cloud systems, given assumptions about 

the hydrometeor distributions.

Relationship to Other TRMM Products

This product is used to validate the vertical hydrometeor and latent heating retrievals of the TMI 

(2A-12), PR (2A-25), and combined product (2B-31) 

vertical hydrometeor and latent heating retrievals.

Data Set Evolution

Pre-operational tests within TSDIS began in August 

1995 and continued until the launch of TRMM in 

November 1997.

Suggested Reading

Yuter, S. E., and R. A. Houze, Jr., 1995.

TRMM Instantaneous Radar Site 3-D 

Reflectivities Product Summary

Coverage: 150-km radius of ground validation 

radar, continuous

Spatial/Temporal Characteristics: 2 km horizontal (minimum), 1.5 km vertical (minimum)

Key Geophysical Parameters: 3-D reflectivity

Processing Level: 2A

Product Type: Validation, at-launch

Maximum File Size: 19 MB

File Frequency: 1/hour

Primary Data Format: HDF

Browse Available: Yes

Additional Product Information: 

trmm.gsfc.nasa.gov

tsdis.gsfc.nasa.gov

DAAC: Goddard Space Flight Center Earth 

Sciences DAAC, daac.gsfc.nasa.gov

Science Team Contact: R. Houze
92 Data Products Handbook - Volume 1 TRMM • Precipitation, Atmospheric Humidity and Atmospheric Temperatures 

TRMM 5-Day Site Rain Map 

(TRMM 3A-53)

Product Description

5-day, 2 km × 2 km surface rainfall at site.

Research and Applications

Significant variations in tropical rainfall patterns 

have been found to occur at periods longer than 5 

days. These patterns frequently have large-scale 

structure and persistence that could be useful for 

weather prediction and validation of general circulation models.

Relationship to Other TRMM Products

Although TRMM sampling by itself is not sufficient 

to accurately represent 5-day rainfall totals, supplementary observations of proxy rainfall data observed 

continuously by other satellites can be calibrated regionally by the validation product to globally map 

and monitor variations in rainfall patterns occurring 

at periods in the range of weeks to a month or two. 

Directly, this product will validate the estimates for 

the TRMM and other satellite algorithms (3B-42 

and 3B-43).

Suggested Reading

Huffman, G. J. et al., 1995.

Rosenfeld, D. et al., 1994.

Steiner, M. et al., 1995.

TRMM 5-Day Site Rain Map Product 

Summary

Coverage: 150-km radius of ground validation 

radar, continuous

Spatial/Temporal Characteristics: 5-day accumulations, 2 × 2 km

Key Geophysical Parameters: Surface rainfall

Processing Level: 3

Product Type: Validation, at-launch

Maximum File Size: 500 KB

File Frequency: 1/5 days

Primary Data Format: HDF

Browse Available: Yes

Additional Product Information: 

trmm.gsfc.nasa.gov

tsdis.gsfc.nasa.gov

DAAC: Goddard Space Flight Center Earth 

Sciences DAAC, daac.gsfc.nasa.gov

Science Team Contact: R. Houze
Precipitation, Atmospheric Humidity and Atmospheric Temperatures • TRMM Data Products Handbook - Volume 1 93

TRMM 30-Day Site Rain Map 

(TRMM 3A-54)

Product Description

30-day, 2 km × 2 km surface rainfall at site.

Research and Applications

This product is used to validate the satellite algorithms and to provide independent validation to 

climate and forecast models.

Relationship to Other TRMM Products

This product is the principal means of validating all 

the TRMM monthly 5° × 5° rainfall estimates.

Suggested Reading

Huffman, G. J. et al., 1995.

Rosenfeld, D. et al., 1994.

Steiner, M. et al., 1995.

TRMM 30-Day Site Rain Map Product 

Summary

Coverage: 150-km radius of ground validation 

radar, continuous

Spatial/Temporal Characteristics: 30-day 

accumulations, 2 × 2 km

Key Geophysical Parameters: Rain vertical 

structure and latent heating

Processing Level: 3

Product Type: Validation, at-launch

Maximum File Size: 500 KB

File Frequency: 1 per month

Primary Data Format: HDF

Browse Available: Yes

Additional Product Information: 

trmm.gsfc.nasa.gov

tsdis.gsfc.nasa.gov

DAAC: Goddard Space Flight Center Earth Sciences DAAC, daac.gsfc.nasa.gov

Science Team Contact: R. Houze

TRMM Monthly 3-D Structure 

(TRMM 3A-55)

Product Description

This product contains vertical profiles of the mean 

reflectivity and the frequency distribution at each 

height. The frequency distribution is provided in the 

form of contoured frequency by altitude diagrams 

(CFADs). Vertical profiles and CFADs are provided 

for the following categories: total, total-land, totalocean, total-convective, total-stratiform, total-anvil, 

land-convective, land-stratiform, land-anvil, oceanconvective, ocean-stratiform, and ocean-anvil.

Research and Applications

The 3-D structure of radar reflectivity is determined 

by hydrometeor distributions within cloud systems 

and is indicative of both microphysical and dynamical 

processes within such systems. The reflectivity structure can be used to simulate satellite observations of 

precipitating cloud systems, given assumptions about 

the hydrometeor distributions.

Relationship to Other TRMM Products

This product is necessary to cross-check the vertical 

structure retrieved by TRMM satellite instruments 

(3A-25, 3A-26, 3B-31).

Suggested Reading

Keenan, T. D. et al., 1988.

Yuter, S. E., and R. A. Houze, Jr., 1995.
94 Data Products Handbook - Volume 1 • Precipitation, Atmospheric Humidity and Atmospheric Temperatures 

TRMM Monthly 3-D Structure Product 

Summary

Coverage: 150-km radius of ground validation 

radar, continuous

Spatial/Temporal Characteristics: 30-day accumulations, 2 × 2 km

Key Geophysical Parameters: 3-D reflectivity

Processing Level: 3

Product Type: Validation, at-launch

Maximum File Size: 400 KB

File Frequency: 1/month

Primary Data Format: HDF

Browse Available: Yes

Additional Product Information: 

trmm.gsfc.nasa.gov

tsdis.gsfc.nasa.gov

DAAC: Goddard Space Flight Center Earth 

Sciences DAAC, daac.gsfc.nasa.gov

Science Team Contact: R. Houze

TRMM
Precipitation, Atmospheric Humidity and Atmospheric Temperatures • Data Products Handbook - Volume 1 95

amounts are derived from the transmittances based 

on theoretical radiative-transfer calculations and 

using look-up-table procedures. MODIS is the first 

space instrument to use near-IR bands together with 

the traditional IR bands to retrieve total precipitable 

water. Experience in this retrieval is based on the 

AVIRIS instrument aboard the NASA ER-2 aircraft. 

Atmospheric water vapor is determined with an accuracy of 5-10%.

 The thermal column water-vapor parameter is derived by integrating the moisture profile through the 

atmospheric column. Other, split-window, methods 

also exist. This class of techniques uses the difference 

in water-vapor absorption that exists between channel 

31 (11 μm) and channel 32 (12 μm).

 Data validation is conducted by comparing these 

data with water-vapor measurements from the National Weather Service (NWS) radiosonde network, 

from ground-based upward-looking microwave 

radiometers, from a ground-based GPS network, 

and from a ground-based sunphotometer network. 

Quality control is performed in two dimensions. The 

first is comparisons of specific validation sites across 

as many different climatic and geographic regions as 

possible. The second is a statistical analysis of the 

entire data set.

 The related MODIS Precipitable Water ATBDs 

can be found in PDF format at eos.nasa.gov/atbd/

modistables.html.

Suggested Reading

Gao, B. C., and A. F. H. Goetz, 1990.

Gao, B. C., and Y. J. Kaufman, 2003.

Green, R. O., and J. E. Conel, 1995.

Jedlovec, G. J., 1987.

Kaufman, Y. J., and B. C. Gao, 1992.

King, M. D. et al., 1992.

King, M. D. et al., 2003.

Kleepsies, T. J., and L. M. McMillan, 1984.

MODIS Total Precipitable Water 

(MOD05)

Product Description

The MODIS Precipitable Water product (MOD05) 

consists of column water-vapor amounts. During the 

daytime, a near-infrared algorithm is applied over 

clear land areas of the globe and above clouds over 

both land and ocean. Over clear ocean areas, watervapor estimates are provided over the extended glint 

area. An infrared algorithm for deriving atmospheric 

profiles is also applied both day and night for Level 

2.

 The Level 2 data are generated at the 1-km spatial 

resolution of the MODIS instrument using the nearinfrared algorithm during the day, and at 5 × 5 1-km 

pixel resolution both day and night using the infrared 

algorithm when at least five FOVs are cloud free. The 

infrared-derived precipitable water vapor is generated 

as one component of product MOD07, and simply 

added to product MOD05 for convenience.

 The solar retrieval algorithm relies on observations 

of water-vapor attenuation of reflected solar radiation 

in the near-infrared MODIS bands so that the product 

is produced only over areas where there is a reflective 

surface in the near IR.

Research and Applications

The near-infrared total-column precipitable water is 

very sensitive to boundary-layer water vapor since 

it is derived from attenuation of reflected solar light 

from the surface. This data product is essential to 

understanding the hydrological cycle, aerosol properties, aerosol-cloud interactions, energy budget, 

and climate. Of particular interest is the collection 

of water-vapor data above cirrus cloudiness, which 

has important applications to climate studies. MODIS also provides finer horizontal-scale atmospheric 

water-vapor gradient estimates than are available 

from the Polar-orbiting Operational Environmental 

Satellites (POES).

Data Set Evolution

The near-IR column water-vapor parameter is derived 

from the attenuation by water vapor of near-IR solar 

radiation. Techniques employing ratios of water-vapor-absorbing bands 17, 18, and 19 with the atmospheric window bands 2 and 5. The ratios remove 

largely the effects of variation of surface reflectance 

with wavelength and result in the atmospheric water-vapor transmittances. The column-water-vapor 

Terra / MODIS 
96 Data Products Handbook - Volume 1 • Precipitation, Atmospheric Humidity and Atmospheric Temperatures 

MODIS Total Precipitable Water Summary

Coverage: Global

Spatial/Temporal Characteristics: Varies with 

retrieval technique; 1 km near-infrared daylight 

only, and 5 km infrared day and night

Key Science Applications: Hydrological cycle 

climatology, effect on aerosol and clouds, 

atmospheric correction, characterization of the 

atmosphere

Key Geophysical Parameters: Atmospheric 

total column water vapor

Processing Level: 2

Product Type: Standard, at-launch

Maximum File Size: 20.15 MB (daytime granules), 3.56 MB (nighttime granules) 

File Frequency: 288/day

Primary Data Format: HDF-EOS

Browse Available: 

modis-atmos.gsfc.nasa.gov/MOD08_D3/

browse.html

Additional Product Information:

modis-atmos.gsfc.nasa.gov/MOD05_L2/

index.html

DAAC: Goddard Space Flight Center Earth 

Sciences DAAC, daac.gsfc.nasa.gov

Science Team Contacts: B. C. Gao, Y. J. 

Kaufman, D. Tanré, W. P. Menzel

Terra / MODIS
Precipitation, Atmospheric Humidity and Atmospheric Temperatures • Data Products Handbook - Volume 1 97

Water vapor over the eastern United States on June 24, 2002. The image at top shows total precipitable 

water (in cm) derived from the cloud-free pixels over land using the near-infrared algorithm. The bottom 

image shows total precipitable water (in cm) derived from the thermal infrared algorithm. On the internet, 

visit visibleearth.nasa.gov to view a variety of MODIS data images.

Terra / MODIS
98 Data Products Handbook - Volume 1 Terra / MODIS • Precipitation, Atmospheric Humidity and Atmospheric Temperatures 

MODIS Atmospheric Profiles 

(MOD07)

Product Description

The MODIS Atmospheric Profiles product (MOD07) 

consists of several parameters: they are total-ozone 

burden, atmospheric stability, temperature and 

moisture profiles, and atmospheric water vapor. All 

of these parameters are produced day and night for 

Level 2 at 5 × 5 1-km pixel resolution when at least 

5 FOVs are cloud free.

 The MODIS total-ozone burden is an estimate of 

the total-column tropospheric and stratospheric ozone 

content. The MODIS atmospheric stability consists 

of three daily Level 2 atmospheric stability indices. 

The Total Totals (TT), the Lifted Index (LI), and the 

K index (K) are each computed using the infrared 

temperature- and moisture-profile data, also derived 

as part of MOD07. The MODIS temperature and 

moisture profiles are produced at 20 vertical levels. A 

statistical regression algorithm based on the infrared 

radiative-transfer equation in a cloudless sky is used. 

The MODIS atmospheric water-vapor product is an 

estimate of the total tropospheric column water vapor 

made from integrated MODIS infrared retrievals of 

atmospheric moisture profiles in clear scenes.

Research and Applications

Total-column ozone estimates at MODIS resolution 

are required by MODIS investigators developing atmospheric correction algorithms. This information is 

crucial for accurate land and ocean-surface-parameter 

retrievals. Furthermore, strong correlations have been 

found to exist between the meridional gradient of total 

ozone and the wind velocity at tropopause levels, 

providing the potential to predict the position and 

intensity of jet streams. Total-column ozone monitoring is also important due to the potential harm to 

the environment caused by anthropogenic depletion 

of ozone.

 Atmospheric instability measurements are predictors of convective-cloud formation and precipitation. The MODIS instrument offers an opportunity 

to characterize gradients of atmospheric stability at 

high resolution and greater coverage. Radiosondederived stability indices are limited by the coarse 

spacing of the point-source data, too large to pinpoint 

local regions of probable convection.

 Atmospheric temperature and moisture sounding 

data at high spatial resolution from MODIS and highspectral-resolution sounding data from AIRS (flying 

onboard Aqua, launched May 4, 2002) provide a 

wealth of new information on atmospheric structure 

in clear skies. The profiles are used to correct for 

atmospheric effects for some of the MODIS products (e.g., sea-surface and land-surface temperatures, 

ocean aerosol properties, water-leaving radiances, 

and photosynthetically-active radiation) as well as 

to characterize the atmosphere for global greenhouse 

studies.

 Total-column precipitable-water estimates at MODIS resolution are required by MODIS investigators 

developing atmospheric-correction algorithms. This 

information is crucial for accurate land and ocean 

surface-parameter retrievals. MODIS also provides 

finer horizontal-scale atmospheric water-vapor gradient estimates than are available from the POES 

satellites.

Data Set Evolution

Temperature and moisture profile retrieval algorithms 

are adapted from the International TOVS Processing 

Package (ITPP), taking into account MODISʼ lack 

of stratospheric channels and far higher horizontal 

resolution. The algorithm uses a statistical regression 

using synthetic radiances derived from a global radiosonde data base. The profile retrieval algorithm 

requires calibrated, navigated, and coregistered 1-km 

FOV radiances from MODIS bands with wavelengths 

between 4.47 μm (band 24), and 14.24 μm (band 36). 

The MODIS cloud-mask product (MOD35) is used 

for cloud screening. The algorithm requires NCEP 

model analysis of surface pressure.

 Atmospheric water vapor, or precipitable water, is 

determined by integrating the moisture profile through 

the atmospheric column. Atmospheric stability estimates are derived from the MODIS temperature and 

moisture retrievals contained in this product. Layer 

temperature and moisture values are used to estimate 

the temperature lapse rate of the lower troposphere 

and the low-level moisture concentration. 

 A statistical regression algorithm is used for the 

MODIS total ozone retrieval. Global ozone soundings 

from ozone sondes and SBUV profiles are used for 

training, and an empirical regression of calculated 

MODIS spectral band radiances against ozone mixing 

ratio profiles is created for the retrieval.

 Data validation is conducted by comparing results 

to in situ radiosonde measurements, NOAA HIRS and 

GOES sounder operational retrievals, NCEP analyses, 

and retrievals from the AIRS/AMSU-A/HSB instrument package on the Aqua platform. Comparisons 

are made with ground-based measurements at the 

Atmospheric Radiation Measurement-Cloud and 
Precipitation, Atmospheric Humidity and Atmospheric Temperatures • Data Products Handbook - Volume 1 99

Radiation Testbeds (ARM-CART) at Oklahoma, 

Alaska, and the Tropical Western Pacific. A field 

campaign using a profiler network in the central U.S. 

and an aircraft equipped with the MODIS Airborne 

Simulator (MAS) was initiated in the first year after 

launch and continues to be active.

Suggested Reading

Hayden, C. M., 1988.

King, M. D. et al., 2003.

Li, J. et al., 2000.

Li, J. et al., 2001.

Li, J., and H. L. Huang, 1999.

Prabhakara, C. et al., 1970.

Seemann, S. W. et al., 2003.

Smith, W. L., and F. X. Zhou, 1982.

Smith, W. L. et al., 1985.

Sullivan, J. et al., 1993.

Terra / MODIS 

MODIS Atmospheric Profiles Summary

Coverage: Global, clear-sky only

Spatial/Temporal Characteristics: 5 km

Key Science Applications:

•Ozone: atmospheric correction, prediction of 

cyclogenesis, anthropogenic ozone depletion

•Atmospheric stability: atmospheric correction, prediction of convective cloudiness and 

precipitation, characterization of the atmosphere

•Soundings: atmospheric correction algorithm 

development and use, characterization of the 

atmosphere

•Total-column water vapor: atmospheric- correction algorithm development and use, 

characterization of the atmosphere

Key Geophysical Parameters: Total-column 

ozone, atmospheric stability (Total Totals, Lifted Index, and K index), atmospheric profiles 

of temperature and moisture, atmospheric 

total-column water vapor

Processing Level: 2

Product Type: Standard, at-launch

Maximum File Size: 32.2 MB 

File Frequency: 288/day

Primary Data Format: HDF-EOS

Browse Available: 

modis-atmos.gsfc.nasa.gov/MOD07_L2/

sample.html

Additional Product Information:

modis-atmos.gsfc.nasa.gov/MOD07_L2/

index.html

DAAC: Goddard Space Flight Center Earth 

Sciences DAAC, daac.gsfc.nasa.gov

Science Team Contact: W. P. Menzel
100 Data Products Handbook - Volume 1 Terra / MODIS • Precipitation, Atmospheric Humidity and Atmospheric Temperatures 

Atmospheric temperatures retrieved from Terra-MODIS radiances on May 22, 2001 at 850 hPa (top image) 

and 500 hPa (bottom image) in K. On the internet, visit visibleearth.gsfc.nasa.gov to view a variety of MODIS 

data images.

265

260

255

250

245

295

290

285

280

275

270

265
101

Chapter 6

Cloud and Aerosol 

Properties and 

Radiative Energy Fluxes

TRMM

CERES

Terra

ASTER

CERES

MISR

MODIS
102 Data Products Handbook - Volume 1 Overview • Cloud and Aerosol Properties and Radiative Energy Fluxes 

Cloud and Aerosol Properties and Radiative Energy 

Fluxes – An Overview

Radiation: The flow of radiation from the Sun to the 

Earth, its absorption, reflection, and emission, determines the energy balance of the Earth, and hence 

the nature of the life of its inhabitants. The information from products describing optical and physical 

properties of clouds and aerosols is combined with 

broadband radiation measurements from CERES 

to provide estimates of solar and infrared radiative 

fluxes, and the associated heating and cooling of the 

Earth and the atmosphere. The radiation estimates 

needed to do this are both shortwave and longwave 

fluxes at the surface and at the top of the atmosphere, 

both downwelling and upwelling, so that the net flow 

can be determined.

Product Overview

The physical quantities that must be measured for the 

cloud, aerosol, and radiation categories are discussed 

in the previous section. In this section we relate the 

data products to these measurement needs and explain 

how the product groups support the overall global 

change program. An evolutionary approach to product generation for the CERES cloud and radiation 

products was used to implement a progression from 

ERBE to CERES algorithms. The ERBE program 

has produced a 14-year record of cloud and radiation 

budget information and CERES continues with these 

measurements.

 Six months after the Terra launch (following 

instrument and analysis validation) CERES began 

production of ERBE-Like data products. These TOA 

fluxes use the ERBE analysis algorithms to allow consistent comparisons to historical ERBE data. The first 

24 months after launch were used to develop a new 

set of CERES angular distribution models (ADMs). 

These ADMs are used to convert broadband CERES 

radiances (i.e., radiative energy at a single viewing 

direction) into estimates of broadband hemispheric 

fluxes. The CERES rotating-azimuth-plane scan 

mode was used along with the cloud properties derived using VIRS for CERES/TRMM measurements 

and MODIS for CERES/Terra measurements to derive a new and much more accurate set of ADMs. 

Beginning 24 months after launch, these more accurate CERES estimated TOA fluxes were produced 

in addition to the ERBE-Like data. Finally, starting 

30 months after launch, CERES routinely provides 

Relationship to Global Change

The flow of energy to and from the Earth is modulated by clouds and aerosols. As such, measuring 

and understanding these elements is key to predicting 

climate change and to testing global climate models. 

The technology for accurately measuring radiative 

fluxes, cloud properties, and aerosol properties from 

space has advanced to the point that these observations can be used to greatly advance our ability to 

address critical global change issues. For example, if 

the global climate changes, do clouds act to result in 

a positive or negative feedback? Do aerosols offset 

some of the expected warming due to the increase 

in greenhouse gases? The products described below 

provide key global observations needed to explore 

these questions.

Clouds: A key step in measuring cloud properties 

from space is to determine where clouds exist, how 

they are distributed through the atmosphere, and what 

their dimensions are. Once clouds are identified and 

mapped, cloud properties, such as effective particle 

size, thermodynamic phase (water, ice), cloud top 

properties, and optical thickness (which all help determine how much radiation passes through them), 

may be measured. The MODIS, MISR, and ASTER 

instruments on the Terra satellite and the VIRS instrument on TRMM provide these key cloud measurements. The daily observations provided by these 

instruments also provide the sampling needed to drive 

and verify climate models.

Aerosols: The measurement of aerosols, suspended 

particles in the atmosphere (e.g., dust, sulfate, smoke, 

etc.), is an important element in describing energy 

transmission through the atmosphere. Aerosols are a 

significant source of uncertainty in climate modeling 

because they affect cloud microphysics by acting as 

condensation nuclei, thereby affecting cloud radiative 

properties, the hydrological cycle and atmospheric 

dynamics. They also interact directly with solar 

radiation, thus affecting the radiative balance. The 

location of anthropogenic aerosols is also an important consideration in their impact on climate. Aerosol 

products described here, derived from MODIS and 

MISR on Terra, provide new information on aerosol 

formation, distribution, and sinks over both the land 

and ocean.
Cloud and Aerosol Properties and Radiative Energy Fluxes • Overview Data Products Handbook - Volume 1 103

estimates of radiative fluxes at the surface and at a 

selected number of levels within the atmosphere. The 

number of atmospheric levels depends on the accuracies determined during post-launch validation.

 All the CERES products except the ERBE-like 

products are classed as “post-launch” for the reasons 

discussed above. The MODIS, MISR, and ASTER 

products are “at-launch” except for the gridded MISR 

products (MIS07, MIS08) and AST13. The following 

section discusses how these products satisfy the 

measurement needs in the three categories: cloud 

products, aerosol properties, and radiant energy flux 

products.

 The cloud products provide the parameters needed 

to determine how much solar radiation reaches the 

Earth and how much escapes back to space. The 

parameters also supply information on factors that 

influence cloud formation and forces driving circulation and global climate. The cloud products fall 

into two classes: 1) cloud detection and delineation, 

and 2) cloud property retrieval. Cloud detection and 

delineation produces masks indicating where clouds 

exist and information on layering and overlap to give 

a geometric picture of global cloud coverage. Cloud 

property retrieval quantifies the physics of the cloud 

by means of parameters such as optical thickness, 

temperature, liquid water content, ice water content, 

particle radius, cloud top altitude, and phase, which 

all relate to the radiative transmission, reflection, and 

emission of the cloud.

 The CERES cloud detection process uses either 

TRMM VIRS or Terra MODIS observations to produce a global binary mask indicating the presence or 

absence of a cloud in each pixel. The MISR cloud 

products provide along-track directional reflectance 

measurements of clouds that will enable development of bidirectional models for different cloud 

types, especially for horizontally inhomogeneous 

cloud fields. Stereo matching of MISR multi-angle 

imagery enables cloud top elevations to be obtained. 

These parameters are provided in products MIS04 and 

MIS07. The ASTER cloud product (AST13) also provides stereo views of cloud cover, which complement 

the MISR data and will permit cloud top elevation 

estimation. The major advantage of the ASTER cloud 

product is the high spatial resolution that allows the 

study of individual cloud cells. The disadvantage is 

very limited spatial and time coverage. The ASTER 

products are also tuned to the difficult polar cloud 

detection task, which will include the detection of 

water clouds and ice clouds. The MODIS cloud 

mask (MOD35) is a global product that provides a 

probability that a view of the Earth is obstructed by 

cloud and whether cloud shadow is detected. It also 

provides information on the processing path taken 

by the algorithm (land/sea, day/night, etc.), along 

with individual spectral test results used in the determination of the final cloud mask product.

 Cloud physical properties are obtained using a set 

of algorithms operating on multispectral imagery data 

from multiple sources including MODIS or VIRS. 

CERES cloud properties include cloud layer mapping, 

cloud top and cloud base pressure, infrared emissivity, 

liquid water path, particle radius, and cloud overlap, 

all derived from MODIS or VIRS data and remapped 

onto the broadband CERES fields of view for studies 

of the Earthʼs atmosphere energy distribution. The 

MODIS cloud property parameters include particle 

size and phase, optical thickness, cloud top height, 

emissivity and temperature, and cloud fraction in a 

region, all of which support specific MODIS science 

tasks that complement the radiation studies and products from other instruments.

 Aerosol Properties are provided by MODIS 

(MOD04) and MISR (MIS05). Aerosols must be 

accounted for during the retrieval of surface and 

atmospheric parameters. The MODIS aerosol 

products document one of the most elusive and 

least understood radiatively active components of 

the atmosphere by providing information on aerosol optical thickness, aerosol size distribution, and 

aerosol sources and sinks. MISR provides aerosol 

information on absorption and scattering in the lower 

atmosphere that aids in the understanding of how 

sunlight is absorbed by aerosols, determining global 

aerosol budgets, and improving correction for aerosol 

effects in the retrieval of surface parameters.

 The Radiant Energy Flux Products provide estimates of the radiative flux downward and upward at 

the top-of-atmosphere, at the Earthʼs surface, and at 

selected intermediate altitudes, as well as the net flux, 

which is used to determine the radiation budget. This 

budget determines the overall heating and cooling of 

the atmosphere and Earthʼs surface and subsequent 

changes in climate. These parameters are generated 

from CERES data and from the final averaged flux 

and radiation budget products AVG, ZAVG and CRH. 

The radiation algorithms use the cloud and aerosol 

products in the process of determining how much 

solar radiation is transmitted, reflected, and absorbed 

in the cloud/atmosphere/Earth surface system, and 

are thus critical to the success of the global climate 

monitoring task.
104 Data Products Handbook - Volume 1 Overview • Cloud and Aerosol Properties and Radiative Energy Fluxes • Cloud and Aerosol Properties and Radiative Energy Fluxes 

Product Interdependency and 

Continuity with Heritage Data

The products from these instruments, especially 

the cloud products from MODIS, CERES, MISR, 

and ASTER, are interrelated on a number of levels. 

First, many algorithms require output products 

from other instruments as ancillary input data. For 

example, CERES cloud parameters (SSF) require 

MODIS and VIRS ancillary radiances such as the 

cloud products (MOD06) and microwave water 

path (MWP). MISR aerosol optical depth products 

require MODIS input. The MODIS cloud mask may 

be used in the viewing decisions of high-resolution 

instruments like ASTER. In many cases, output data 

from one instrument become input data for another. 

This permits the formatting necessary to permit the 

generation of higher-level products, and does not 

represent a duplication of data products.

 Second, surface-viewing instruments use ancillary 

data in atmospheric correction algorithms to reduce 

contamination of surface retrievals. Similar products from different instruments may use different 

algorithms and are useful for mutual comparison 

and validation. Finally, a degree of redundancy for 

principal (especially cloud) products used as ancillary input is necessary to permit alternative sources 

of ancillary data in the event of instrument failure or 

degradation.

 The CERES instruments on TRMM, Terra, and 

Aqua are a continuation of the ERBE project, 

extending a heritage of radiation budget products 

dating back more than a decade. Several CERES 

data products use ERBE algorithms to ensure continuity. As with other EOS-era missions, the data sets 

produced by continuing past missions will provide 

valuable information on long-term trends. Analysis 

of the trends will provide valuable input to longterm predictive models, and will enable scientists to 

interpret better the possible causes or consequences 

of changes to clouds, aerosols, and radiation budget 

on global climate change.

Suggested Reading

Ackerman, S. A. et al., 1998.

Kaufmann, Y. J. et al., 2002.

King, M. D. et al., 1992.

King, M. D. et al., 1997.

King, M. D. et al., 2003.

Kummerow, C. et al., 1998.

Kummerow, C. et al., 2000.

Platnick, S. et al., 2003.

Wielicki, B. A. et al., 1995.

Wielicki, B. A. et al., 2002.
Cloud and Aerosol Properties and Radiative Energy Fluxes • Data Products Handbook - Volume 1 105

ASTER Polar Surface and 

Cloud Classification Product 

(AST13)

Product Description

This Level 4 product is a polar classification map, 

consisting of the following classes: water, wet/slush 

ice, snow/ice, land, shadow, water cloud, and ice 

cloud. Each pixel is classified using a bit map for 

each of the above classes. Therefore, for complex 

scenes such as cirrus over broken sea ice, the water, 

ice, and ice cloud bits would be set. This product is 

produced at 30-m spatial resolution and uses a combination of visible, near-infrared, and infrared channels. 

The polar regions are defined as all land and water 

regions lying poleward of 60°N or 60°S. Both daytime and nighttime classifications are available, with 

the daytime algorithm applied for solar zenith angles 

less then 85°, and the nighttime algorithm applied in 

all cases. This is an on-request product.

Research and Applications

Since greenhouse forcings are expected to be amplified in the polar regions, these regions may act 

as early warning indicators of global climate shifts. 

Cloud cover can be expected to be altered by greenhouse forcings, and cloud changes are expected to 

have a significant effect on sea ice conditions and 

regional ice-albedo feedbacks, especially to the polar 

heat balance which directly affects surface melting. 

ASTER polar data will be used to monitor changes 

in surface conditions, notably temperature, albedo, 

and sea ice breakup.

Data Set Evolution

This data set builds on work over the past decade 

with data from Landsat TM, AVIRIS, TIMS, and 

MAS. The improved spectral coverage, resolution, 

and radiometric accuracy enable ASTER to provide 

remotely sensed polar data of unprecedented accuracy.

 The ASTER polar data set are also used to validate the global-scale polar data and cloud property 

retrievals from MODIS. In particular, this data set is 

used to validate the MODIS cloud optical thickness 

and effective particle sizes, which directly impact the 

Earthʼs radiative budget. Only ASTER has the spatial 

resolution necessary to fully analyze the cloud 3-D 

effects.

Suggested Reading

Ebert, E., 1987.

Tovinkere, V. R. et al., 1993.

Welch, R. M. et al., 1990.

Welch, R. M. et al., 1990.

ASTER Polar Surface and Cloud 

Classification Product Summary

Coverage: Regional (poleward from

60° N or S)

Spatial/Temporal Characteristics: 30 m over 

60 km × 60 km scenes

Key Science Applications: Climate modeling, 

polar heat balance

Key Geophysical Parameters: Polar clouds, 

thick cloud, thin cloud, shadow, ice, wet ice

Processing Level: 4

Product Type: Standard, on-request

Maximum File Size: 42 MB

File Frequency: On demand 

Primary Data Format: HDF-EOS

Browse Available: Yes

Additional Product Information:

edcdaac.usgs.gov/aster/asterdataprod.html

DAAC: EDC Land Processes DAAC, 

edcdaac.usgs.gov

Science Team Contact: R. Welch

Terra / ASTER
106 Data Products Handbook - Volume 1 • Cloud and Aerosol Properties and Radiative Energy Fluxes 

Research and Applications

This product is a CERES-produced equivalent to 

the instantaneous fluxes derived from the ERBE 

scanners on ERBS, NOAA-9, and NOAA-10. ES-8 

flux data are directly comparable to ERBE fluxes and 

thus effectively extend the time series of ERBE flux 

measurements, allowing global-change experiments 

to continue using ERBE algorithms without the errors 

associated with comparing fluxes estimated by two 

different algorithms. Improved TOA flux-estimation 

algorithms for CERES (using improved ADMs and 

VIRS/MODIS cloud identification) are available in 

the Single-Scanner Footprint (SSF) product. Producing the two versions of TOA fluxes will allow 

continuity with the previous ERBE data along with 

improved accuracy of CERES TOA flux data.

Data Set Evolution

The ERBE-like Inversion Subsystem consists of algorithms that convert filtered radiometric measurements 

to instantaneous flux estimates at the TOA. The basis 

for this procedure is the ERBE Data Management 

System, which produced TOA fluxes from the 

ERBE scanning radiometers. The system consists of 

algorithms for Spectral Correction, Observed Scene 

TRMM and Terra / CERES

CERES ERBE-like 

Instantaneous TOA Estimates 

(ES-8)

Product Description

The ERBE-like Instantaneous TOA Estimates (ES-8) 

product contains 24 hours of instantaneous CERES 

data for a single scanner instrument. The ES-8 product contains filtered radiances recorded every 0.01 

seconds for the total, shortwave (SW), and window 

(WN) channels and the unfiltered SW, longwave 

(LW), and WN radiances. The SW and LW radiances 

at spacecraft altitude are converted to top-of-the-atmosphere (TOA) fluxes with a scene-identification 

algorithm and Angular Distribution Models (ADMs), 

which are “like” those used for the Earth Radiation 

Budget Experiment (ERBE). The TOA fluxes, scene 

identification, and angular geometry are included in 

the ES-8 product.

 A complete listing of parameters for this data 

product can be found in the CERES Data Products 

Catalog at asd-www.larc.nasa.gov/DPC/DPC.html, 

and detailed definitions of each parameter can 

be found in the CERES Collection Guide at asdwww.larc.nasa.gov/ceres/collect_guide/list.html.

The image above shows thermal emitted longwave flux of energy escaping the top of the Earthʼs atmosphere 

as measured by the Terra CERES instrument on May 25, 2001. Radiative flux is the sum of energy emitted 

by the earth in all angles. Compare to the CERES BDS longwave radiance figure for the same day to show 

the reduction in orbit banding as directional radiances are converted to radiative fluxes. The ES-8 product 

contains both thermal emitted energy as well as solar reflected energy to allow studies of the Earthʼs radiative 

energy balance at the top of the atmosphere that can be compared with the ERBE experiment data from 

the 1980s. This is also the earliest validated data product available from each CERES instrument.

70 150 230 310 390

Outgoing Longwave Radiation (Watts per square meter)
Cloud and Aerosol Properties and Radiative Energy Fluxes • Data Products Handbook - Volume 1 107

CERES ERBE-like Monthly 

Regional Averages (ES9) and ERBE-like Monthly 

Geographical Averages (ES-4)

Product Description

The ERBE-like Monthly Regional Averages (ES-9) 

product contains a month of space-and-time-averaged CERES data for a single scanner instrument. 

The ES-9 product is also produced for combinations 

of scanner instruments. All instantaneous shortwave 

and longwave fluxes at the top of the atmosphere 

(TOA) from the CERES ES-8 product for a month 

are sorted by 2.5° spatial regions, by day number, 

and by the local hour of observation. The mean of 

the instantaneous fluxes for a given region-day-hour 

bin is determined and recorded in the ES-9 product 

along with other flux statistics and scene information. 

For each region, the daily average flux is estimated 

from an algorithm that uses the available hourly 

data, scene identification data, and diurnal models. 

This algorithm is “like” the algorithm used for the 

Earth Radiation Budget Experiment (ERBE). The 

ES-9 product also contains hourly average fluxes 

for the month and an overall monthly average for 

each region. These average fluxes are given for both 

clear-sky and total-sky scenes.

 The ERBE-like Monthly Geographical Averages 

(ES-4) product contains a month of space-and-timeaveraged CERES data for a single scanner instrument. 

The ES-4 product is also produced for combinations 

of scanner instruments. For each observed 2.5-degree 

spatial region, the daily average, the hourly average 

over the month, and the overall monthly average of 

shortwave and longwave fluxes at the TOA from the 

CERES ES-9 product are spatially nested up from 

2.5° regions to 5° and 10° regions, to 2.5°, 5°, and 10° 

zonal averages, and to global monthly averages. For 

each nested area, the albedo and net flux are given. 

For each region, the daily average flux is estimated 

from an algorithm that uses the available hourly data, 

scene-identification data, and diurnal models. This algorithm is “like” the algorithm used for the ERBE.

 A complete listing of parameters for these data 

products can be found in the CERES Data Products 

Catalog at asd-www.larc.nasa.gov/DPC/DPC.html, 

and detailed definitions of each parameter can 

be found in the CERES Collection Guide at asdwww.larc.nasa.gov/ceres/collect_guide/list.html.

Type, Radiance-to-Flux Conversion using Angular 

Distribution Models, and finally the Regional Averaging algorithm, which produces regional fluxes. A 

linear-estimation scheme is used to relate the filtered 

and unfiltered radiances. Ancillary parameter files are 

required for processing, including ADMs and spectral 

correction coefficients for converting filtered radiances to unfiltered radiances.

 Documentation on the ES-8 algorithms can be 

found in CERES ATBD Subsystem 2.0, in PDF format, at eospso.gsfc.nasa.gov/atbd/cerestables.html.

Suggested Reading

Avis, L. M. et al., 1984.

Barkstrom, B. R., and G. L. Smith, 1986.

Smith, G. L. et al., 1986.

Wielicki, B. A. et al., 1998.

CERES ERBE-like Instantaneous TOA 

Estimates Summary

Coverage: Global

Spatial/Temporal Characteristics: 20 km at 

nadir/0.01 second

Key Geophysical Parameters: TOA SW, 

LW fluxes and radiances, scene type

Processing Level: 2

Product Type: Standard, at-launch

Maximum File Size: 490 MB

File Frequency: 1/day

Primary Data Format: HDF

Browse Available: No

Additional Product Information: 

eosweb.larc.nasa.gov/PRODOCS/ceres/

table_ceres.html

DAAC: Langley Atmospheric Sciences Data 

Center, eosweb.larc.nasa.gov

Science Team Contacts: N. G. Loeb,

K. J. Priestley

TRMM and Terra / CERES
108 Data Products Handbook - Volume 1 TRMM and Terra / CERES • Cloud and Aerosol Properties and Radiative Energy Fluxes 

Research and Applications

These products are designed to provide continuity 

with the historical ERBE radiation budget data. 

They use the same algorithms to provide the most 

consistent possible analysis of changes in radiation 

budget from the 80s through the EOS era. These 

products are often used to verify the accuracy and 

limitations of climate models, especially with respect 

to the effect of clouds on radiation. These CERES 

products are some of the earliest available, but lack 

the accuracy improvements, surface radiative fluxes, 

atmosphere radiative fluxes, and matching cloud and 

aerosol information in the more advanced CERES 

SRBAVG and AVG products.

 The ES-9 and ES-4 products are equivalent to their 

ERBE analogs (S-9, and S-4) and effectively extend 

the time series of ERBE flux measurements through 

the EOS era. Various spatial scales are provided to 

simplify intercomparisons with earlier Nimbus and 

ERBE non-scanner time series.

Data Set Evolution

These data sets are generated using heritage ERBE algorithms operating on inputs from CERES calibrated 

Level 1 data and ancillary data. Data processing uses 

CERES Subsystem 3.0, which temporally interpolates 

CERES measurements using linear interpolation over 

oceans and half-sine-curve fit over land and desert 

regions. Monthly and monthly-hourly means are 

then computed using the combination of observed 

and interpolated values.

 Documentation on the algorithms used to create 

the data products can be found in CERES ATBD 

Subsystem 3.0, in PDF format, at eos.nasa.gov/atbd/

cerestables.html.

Suggested Reading

Brooks, D. R. et al., 1986.

Harrison, E. F. et al., 1990a,b.

Harrison, E. F. et al., 1995.

Ramanathan V. et al., 1989.

Wielicki, B. A. et al., 1998.

CERES ERBE-like Monthly Regional 

Averages and ERBE-like Monthly 

Geographical Averages Products Summary

Coverage: Global

Spatial/Temporal Characteristics: 2.5°, 5.0°, 

10.0°, region and zone, global/monthly 

(by day and hour)

Key Geophysical Parameters: TOA shortwave 

and longwave radiant flux, scene type, albedo, 

solar incidence, cloud condition

Processing Level: 3

Product Type: Standard, at-launch

Maximum File Size: 27 MB (ES-4), 1100 MB 

(ES-9)

File Frequency: 1/month

Primary Data Format: HDF

Browse Available: No

Additional Product Information:

eosweb.larc.nasa.gov/PRODOCS/ceres/table_

ceres.html

DAAC: Langley Atmospheric Sciences Data 

Center, eosweb.larc.nasa.gov

Science Team Contacts: D. F. Young, T. Wong

(Left ) Sample CERES ERBE-like Monthly Geographical Averages, presenting the TOA longwave flux for March 2000. (Right) Sample 

CERES ERBE-like Monthly Regional Average, presenting the TOA clear-sky longwave flux for March 2000. Both images are from data 

from the Terra CERES. On the internet, visit visibleearth.nasa.gov to view a variety of CERES data images.

No 220 231 242 253 264 275 286 297 308 319 330

Data Watts per square meter No 150 168 186 204 222 240 258 276 294 312 330

Data Watts per square meter
Cloud and Aerosol Properties and Radiative Energy Fluxes • Data Products Handbook - Volume 1 109

CERES Single Scanner TOA/

Surface Fluxes and Clouds 

(SSF)

Product Description

The Single Scanner Footprint TOA/Surface Fluxes 

and Clouds (SSF) product contains one hour of 

instantaneous CERES data for a single scanner instrument. The SSF combines instantaneous CERES 

data with scene information from a higher-resolution 

imager such as the Visible/Infrared Scanner (VIRS) on TRMM or MODIS on 

Terra and Aqua. Scene identification 

and cloud properties are defined at the 

higher imager resolution and these data 

are averaged over the larger CERES footprint. For each CERES footprint, the SSF 

contains the number of cloud layers, and, 

for each layer, the cloud amount, height, 

temperature, pressure, optical depth, 

emissivity, ice and liquid-water path, and 

water-particle size. The SSF also contains 

the CERES filtered radiances for the total, shortwave (SW), and window (WN) 

channels and the unfiltered SW, longwave 

(LW), and WN radiances. The SW, LW, 

and WN radiances at spacecraft altitude 

are converted to top-of-the-atmosphere 

(TOA) fluxes based on the imager-defined 

scene. These TOA fluxes are used to estimate surface fluxes. Only footprints with 

adequate imager coverage are included 

on the SSF, which is much less than the full set of 

footprints on the CERES ES-8 product.

 A complete listing of parameters for this data 

product can be found in the CERES Data Products 

Catalog at asd-www.larc.nasa.gov/DPC/DPC.html, 

and detailed definitions of each parameter can 

be found in the CERES Collection Guide at asdwww.larc.nasa.gov/ceres/collect_guide/list.html. The 

algorithms are described in CERES ATBDs 4.0-4.6, 

located, in PDF format, at 

eospso.gsfc.nasa.gov/atbd/cerestables.html.

TRMM and Terra / CERES

The CERES SSF provides a new level of integration and accuracy in radiation budget data. 

Cloud imager data from VIRS on TRMM and 

MODIS on Terra are used to derive the cloud 

and surface properties accurately matched to 

each CERES field of view. These properties 

are then used with a new generation of angular 

dependence models developed from the CERES 

hemispheric scanning capability to provide 

much more accurate TOA fluxes. These images 

show results from Terra SSF for March 22, 

2001: TOA shortwave reflected flux (top left), 

cloud fraction (top right), and the difference 

in reflected flux from the simpler ERBE-Like 

TOA flux on the ES-8 data product to the more 

accurate fluxes on the SSF product (left). One 

of the major new capabilities of the SSF product 

is the ability to provide accurate fluxes for individual cloud or surface types, where the simpler 

ERBE data was only accurate for large ensemble 

monthly means.

On the internet, visit visibleearth.nasa.gov to 

view a variety of CERES data images. -30 -24 -18 -12 -6 0 6 12 18 24 30

Shortwave Irradiance Difference (Watts per square meter)

0 60 120 180 240 300 360 420 480 540 600

Shortwave Irradiance (Watts per square meter)

0 10 20 30 40 50 60 70 80 90 100

Cloud Fraction
110 Data Products Handbook - Volume 1 TRMM and Terra / CERES • Cloud and Aerosol Properties and Radiative Energy Fluxes 

Research and Applications

The SSF data product has instantaneous cloud, 

aerosol, TOA radiative fluxes, and simple parameterization surface radiative fluxes for each CERES 

field of view. This data set is optimal for research 

on the radiative properties of different types of 

cloudiness, aerosols, or surface properties. It can be 

compared to numerical weather prediction models, 

cloud resolving models, and field experiment data. 

The properties and the fluxes are matched so that 

space/time matching noise is reduced to 1% or less. 

The product also adds surface radiative flux estimates 

from simple radiative transfer parameterizations that 

attempt to depend as directly as possible on the TOA 

fluxes. More rigorous full radiative transfer constrained surface and atmosphere fluxes are found in 

the CRS data product. SSF provides the basis for the 

remainder of the new generation of radiation budget 

data products that go far beyond the earlier ERBE 

capability.

Data Set Evolution

This product is the first step in the sequence of processes from the geolocated radiances to estimates of 

the global radiation budget.

 The SSF analysis uses coarse-spatial-resolution 

broadband radiance data from CERES, matched 

with simultaneous high-spatial-resolution narrowband radiance data from MODIS on Aqua.

Analysis proceeds in five basic steps:

1) Cloud masking and clear-sky map update;

2) Simultaneous determination of cloud height, 

layer, phase, and optical properties;

3) Convolution of imager pixel-level cloud/surface 

properties with the CERES point-spread function 

for each CERES field of view;

4) Use of convolved cloud/surface properties to select a broadband anisotropic model for conversion 

of CERES radiance to TOA flux (Loeb, N. et al., 

2003); and

5) Estimation of radiative fluxes at the surface, using theoretical/statistical relationships to TOA 

fluxes.

The SSF data product contains the matched cloud 

and radiative-flux data, which will also be used for 

later determination of radiative fluxes within the 

atmosphere.

Suggested Reading

Dong, X. et al., 1999.

Green, R. N., and B. A. Wielicki, 1995.

Green, R. N. et al., 1995.

Loeb, N. et al., 2003.

Minnis, P. et al., 1995.

Minnis, P. et al., 1998.

Minnis, P. et al., 1999.

Wielicki, B. A. et al., 1995.

Wielicki, B. A. et al., 1998.

Young, D. F. et al., 1998.

CERES Single Scanner TOA/Surface 

Fluxes and Clouds Summary

Coverage: Global

Spatial/Temporal Characteristics: 20 km at 

nadir/0.01 second

Key Geophysical Parameters: Radiance and 

flux (TOA and surface); cloud and clear-sky 

statistics, optical depth, infrared emissivity, 

liquid-water path, ice-water path, cloud-top 

pressure, cloud effective pressure, cloud 

temperature and cloud height, cloud-bottom 

pressure, water and ice-particle radius

Processing Level: 2

Product Type: Standard, post-launch

Maximum File Size: 260 MB

File Frequency: 1/hour

Primary Data Format: HDF

Browse Available: No

Additional Product Information: 

eosweb.larc.nasa.gov/PRODOCS/ceres/

table_ceres.html

DAAC: Langley Atmospheric Sciences Data 

Center, eosweb.larc.nasa.gov

Clouds Science Team Contact: P. Minnis

Angular Models Science Team Contact:

Norman Loeb
Cloud and Aerosol Properties and Radiative Energy Fluxes • Data Products Handbook - Volume 1 111

LW Down 455 349.3 -3.0 18.4 17 303.9 -4.8 8.4

LW Up 430 416.3 -2.8 16.4 17 397.8 5.9 11.4

SW Down 260 427.9 -21.2 60.3 17 324.5 -14.3 17.1

SW Up 260 87.2 10.8 19.9 17 71.6 5.6 13.0

N Mean Bias (Obs-CRS) RMS N Mean Bias (Obs-CRS) RMS

LW Up 457 246.7 -0.4 4.0 LW Up 17 273.9 -1.8 3.2

SW Up 260 225.5 -2.2 8.1 SW Up 17 108.8 1.2 1.3

All Sky Clear Sky

N Mean Bias (Obs-CRS) RMS

All Sky

LW Down

LW Up 

SW Down 

SW Up 

N Mean Bias (Obs-CRS) RMS

Clear Sky

CERES Clouds and Radiative 

Swath (CRS)

Product Description

The Clouds and Radiative Swath (CRS) product 

contains one hour of instantaneous CERES data for 

a single scanner instrument. The CRS contains all of 

the CERES SSF product data. For each CERES footprint on the SSF the CRS also contains vertical flux 

profiles evaluated at four levels in the atmosphere: 

the surface, 500-, 70-, and 1-hPa. The CRS fluxes 

and cloud parameters are adjusted for consistency 

with a radiative-transfer model, and adjusted fluxes 

are evaluated at the four atmospheric levels for both 

clear-sky and total-sky.

 A complete listing of parameters for this data 

product can be found in the CERES Data Products 

Catalog at asd-www.larc.nasa.gov/DPC/DPC.html.

Research and Applications

The surface and atmospheric radiation budget 

(SARB) is the primary driver of the hydrological 

cycle and the general circulation of the atmosphere. 

Anthropogenically induced changes in the radiatively 

active trace gases and aerosols will affect the SARB 

and will therefore force a climate response. The 

68 radiance and radiant flux and cloud parameters 

provided by this product are inputs to higher level 

algorithms which produce gridded, averaged flux 

and cloud-property products. This product contains 

a further enhancement of the basic data on radiation and cloud characteristics from SSF needed for 

determination of the amount of energy passing from 

space through the atmosphere to the Earth and flowing 

from the Earth back to space, which is provided by 

products SRBAVG, AVG, and ZAVG. It is a global 

product and is used as the input to the hourly gridded 

computation of surface, multiple atmosphere levels, 

and TOA radiative fluxes.

 The analysis algorithms are described in CERES 

ATBD Subsystem 5.0, which can be found in PDF format at eospso.gsfc.nasa.gov/atbd/cerestables.html. 

 The online CERES ARM Validation Experiment 

(CAVE) provides easy-to-read subsets of these 

CERES products at over 30 ground sites; plots of 

surface radiometer and meteorology time series for 

validation; “point and click” facilities for radiative 

transfer calculations; and a library of the latest reports 

on CERES SARB (www-cave.larc.nasa.gov/cave/).

Data Set Evolution

TRMM and Terra / CERES

Down LW Flux at Surface

Down SW Flux at Surface

600

500

400

300

200

CRS Sfc LW Down (W/m2

) 1200

1000

800

600

400

200

0

CRS Sfc SW Down (W/m2

)

200 300 400 500 600

CAVE Obs Sfc LW Down (W/m2)

- All Sky

- Clear Sky

0 200 400 600 800 1000 1200

CAVE Obs Sfc SW Down (W/m2)

Comparison of CERES/TRMM (Jan-Aug 

1998) Edition-2B SARB calculated downwelling flux to observations at ARM/SGP 

E13 site (ARM Central Facility). Clear Sky 

points in tables are identified clear if both 

CERES (VIRS) cloud mask and surface radiometry cloud amount (Long/Ackerman 

algorithm) equal to 0%. Clear Sky in plots 

are clear by virtue of CERES (VIRS) cloud 

mask equal to 0%. “Obs” refers to surface 

flux measured by up-looking pyrgeometer 

(LW), pyranometer (SW) and at TOA measured by the CERES instrument.

Surface Comparisons*

* Complete comparison results at: www-cave.larc.nasa.gov/cave/ (under “Site Statistics”)

TOA Comparisons*
112 Data Products Handbook - Volume 1 • Cloud and Aerosol Properties and Radiative Energy Fluxes 

CERES Monthly Gridded 

Radiative Fluxes and Clouds 

(FSW)

Product Description

The Monthly Gridded Radiative Fluxes and Clouds 

(FSW) product contains a month of space-and-timeaveraged CERES data for a single scanner instrument. 

The FSW is also produced for combinations of scanner instruments. All instantaneous fluxes from the 

CERES CRS product for a month are sorted by 1° 

spatial regions, by day number, and by the Universal Time (UT) hour of observation. The mean of 

the instantaneous fluxes for a given region-hour 

bin is determined and recorded on the FSW along 

with other flux statistics and scene information. The 

mean adjusted fluxes at the four atmospheric levels 

defined by CRS are also included for both clear-sky 

and total-sky scenes. In addition, four cloud-height 

categories are defined by dividing the atmosphere 

into four intervals with boundaries at the surface, 

700-, 500-, 300-hPa, and the top of the atmosphere 

(TOA). The cloud layers from CRS are put into one 

of the cloud-height categories and averaged over the 

region. The cloud properties are also column averaged 

and included on the FSW.

 A complete listing of parameters for this data 

product can be found in the CERES Data Products 

Catalog at asd-www.larc.nasa.gov/DPC/DPC.html.

Research and Applications

This product provides a 1° gridded version of the 

CRS data. The data volume is an order of magnitude smaller, but it retains the ability to compare 

to weather or climate model results at 1° resolution. 

It can also serve as a smaller browse product. This 

data set is a primary input to the SYN and AVG data 

products which merge the CERES data with 3 hourly 

geostationary data to provide more accurate diurnal 

cycles and time averages. Care is taken to let the 

geostationary data provide the relative diurnal cycle 

magnitude, but for CERES to provide the absolute 

accuracy of the fluxes.

TRMM and Terra / CERES

The inputs to this product come from the SSF product, 

and the cloud parameters are further corrected and 

adjusted. The TOA and surface values are corrected 

for satellite and cloud effects to produce observed 

TOA and tuned surface estimates. This data set is 

tailored to provide the needed inputs to the hourly 

gridded flux and surface radiation budget Level 3 

product, FSW.

Suggested Reading

Charlock, T. P., and T. L. Alberta, 1996.

Charlock, T. P. et al., 1993.

Charlock, T. P. et al., 1995.

Fu, Q., and K. Liou, 1993.

Liou, K.-N., 1992.

Whitlock, C. H. et al., 1995.

Wielicki, B. A. et al., 1998.

Wu, M., and L.-P. Chang, 1992.

CERES Clouds and Radiative Swath 

Summary

Coverage: Global

Spatial/Temporal Characteristics: 20 km at 

nadir/0.01 second

Key Geophysical Parameters: Shortwave and 

longwave radiant flux at the surface, multiple 

atmosphere levels, and TOA, scene identification, cloud parameters

Processing Level: 2

Product Type: Standard, post-launch

Maximum File Size: 354 MB

File Frequency: 1/hour

Primary Data Format: HDF

Browse Available: No

Additional Product Information: 

eosweb.larc.nasa.gov/PRODOCS/ceres/

table_ceres.html

DAAC: Langley Atmospheric Sciences Data 

Center, eosweb.larc.nasa.gov

Science Team Contact: T. P. Charlock
Cloud and Aerosol Properties and Radiative Energy Fluxes • Data Products Handbook - Volume 1 113

Data Set Evolution

The algorithm is based on ERBE algorithms and 

carries out aggregation of the high-resolution 20-

km (nadir) data products to 1° (110 km) data cells 

using weighted averaging and linear interpolation to 

produce the flux and cloud data in this grid-cell format chosen for final products. This is an equal-angle 

grid consisting of regions that are 1° in latitude by 

1° in longitude. The parameter list is essentially the 

same as for CRS.

 The analysis algorithms are described in CERES ATBD Subsystem 6.0, which can be found at 

eospso.gsfc.nasa.gov/atbd/cerestables.html.

Suggested Reading

Stowe, L. et al., 1993.

Wielicki, B. A. et al., 1998.

TRMM and Terra / CERES

CERES Monthly Gridded Radiative Fluxes 

and Clouds Product Summary

Coverage: Global

Spatial/Temporal Characteristics: 1° region/

hour

Key Geophysical Parameters: Gridded 

surface, atmosphere layer, and TOA fluxes, 

clear-sky and total-sky cloud parameters in 

CRS, (e.g., cloud overlap, cloud pressure, 

cloud altitude, cloud temperature, ice-water 

properties, ice-particle properties, liquid-water 

properties)

Processing Level: 3

Product Type: Standard, post-launch

Maximum File Size: 1130 MB

File Frequency: 11/month

Primary Data Format: HDF

Browse Available: No

Additional Product Information: 

eosweb.larc.nasa.gov/PRODOCS/ceres/

table_ceres.html

DAAC: Langley Atmospheric Sciences Data 

Center, eosweb.larc.nasa.gov

Science Team Contact: D. F. Young

Convergence of longwave flux into the atmospheric layer from the surface to 500hPa level for all-sky conditions. The results are from 

TRMM data for February 3, 1998. Gaps between orbit tracks show the limit of the VIRS imager scan swath. The FSW data product 

contains 1o gridded data for TOA, surface, and atmosphere products along with matching surface, aerosol, and cloud data for each 

grid box. The LW convergence represents the most accurate construction consistent with the CERES LW and window fluxes, VIRS 

cloud properties, and ECMWF temperature and humidity profiles. On the internet, visit visibleearth.nasa.gov to view a variety of 

CERES data images.
114 Data Products Handbook - Volume 1 • Cloud and Aerosol Properties and Radiative Energy Fluxes 

systems which carry CERES simultaneously in orbit 

(TRMM, Terra, and Aqua) provide up to six samples 

per day. This significantly reduces temporal sampling 

errors. Estimates of the cloud properties at synoptic 

times are also provided. The cloud-mask output 

defines clear-sky conditions for the 1° cells for the 

radiative fluxes, which are averaged over 3-hour periods. Interpolation to the synoptic times uses geostationary data to assist in modeling meteorological 

variations between times of observations.

 The analysis algorithms are described in CERES ATBD Subsystem 7.0, which can be found at 

eospso.gsfc.nasa.gov/atbd/cerestables.html.

Suggested Reading

Breigleb, B. P., and V. Ramanathan, 1982.

Brooks, D. R. et al., 1986.

Harrison, E. F. et al., 1988.

Minnis, P., and E. F. Harrison, 1984.

Smith, G. L. et al., 1995.

Wielicki, B. A. et al., 1998.

Young, D. F. et al., 1995a.

CERES Synoptic Radiative Fluxes and 

Clouds Summary

Coverage: Global

Spatial/Temporal Characteristics: 1° region/

3-hour, monthly

Key Geophysical Parameters: Synoptic 

time- interval clear-sky, total-sky fluxes at 

surface, atmosphere levels, and TOA, cloud 

parameters including cloud overlap, cloud 

pressure, cloud altitude, cloud temperature, 

optical depth, ice-water properties, ice-particle 

properties, liquid-water properties, photosynthetically active radiation

Processing Level: 3

Product Type: Standard, post-launch

Maximum File Size: 240 MB

File Frequency: 1/3-hour (3-hour Level 3), 

1/month (Monthly Level 3)

Primary Data Format: HDF

Browse Available: No

Additional Product Information: 

eosweb.larc.nasa.gov/PRODOCS/ceres/

table_ceres.html

DAAC: Langley Atmospheric Sciences Data 

Center, eosweb.larc.nasa.gov

Science Team Contact: T. Wong

TRMM and Terra / CERES

CERES Synoptic Radiative 

Fluxes and Clouds (SYN)

Product Description

The Synoptic Radiative Fluxes and Clouds (SYN) 

product contains a day of space-and-time-averaged 

CERES data for a single scanner instrument. The 

SYN is also produced for combinations of scanner 

instruments. The 1° regional flux at the hour of observation from the CERES FSW product and concurrent 

diurnal data from geostationary satellites are used to 

estimate the regional flux at 3-hour intervals. Also at 

3-hour intervals are estimates of the adjusted fluxes 

at the four atmospheric levels as defined by the CERES CRS product for both clear-sky and total-sky 

scenes, estimates of the average cloud parameters in 

four cloud-height categories, and column-averaged 

cloud parameters.

 A complete listing of parameters for this data 

product can be found in the CERES Data Products 

Catalog at asd-www.larc.nasa.gov/DPC/DPC.html.

Research and Applications

The synoptic format of this product provides data 

which are global and simultaneous in time. This is 

preferred by climate modelers since the synoptic times 

ensure consistency with ground-truth meteorological 

observations from weather stations and radiosondes 

as well as with geostationary satellites, which provide 

images at synoptic hours. The production of a CERES 

synoptic data product is important because synoptic 

views provide a basis for studying the life cycle of 

cloud systems and for validating the CERES data 

processing, and it provides a regular data structure 

that simplifies the design of algorithms and operation of the data processing system. Synoptic fields 

of radiation and clouds are potentially valuable in 

developing and understanding the role of clouds in 

the generation and dissipation of available potential 

energy, since the calculation of this quantity requires 

integration over approximately horizontal layers 

within the atmosphere.

Data Set Evolution

The CERES time-space averaging and time-interpolation algorithm is based on techniques used in 

previous systems for deriving the Earth radiation 

budget, such as ERBE. The chief input to the time 

interpolation process is the gridded SW and LW TOA 

clear-sky and total-sky fluxes and cloud information 

provided by the FSW product. Data from the three 
Cloud and Aerosol Properties and Radiative Energy Fluxes • TRMM and Terra / CERES Data Products Handbook - Volume 1 115

CERES Monthly Regional 

Radiative Fluxes and Clouds 

(AVG) and Monthly Zonal and 

Global Radiative Fluxes and 

Clouds (ZAVG)

Product Description

The Monthly Regional Radiative Fluxes and Clouds 

(AVG) product contains a month of space-and-timeaveraged CERES data for a single scanner instrument. 

The AVG is also produced for combinations of scanner instruments. The 1° regional flux at the hour of 

observation from the CERES SYN product and 

concurrent diurnal data from geostationary satellites 

are used to estimate the daily regional flux, which 

is averaged to yield the monthly average flux. Adjusted fluxes at the four atmospheric levels defined 

by the CERES CRS product are also estimated for 

both clear-sky and total-sky scenes. In addition, 

four cloud-height categories are defined by dividing 

the atmosphere into four intervals with boundaries 

at the surface, 700-, 500-, 300-hPa, and the top of 

the atmosphere. The cloud layers from SYN are put 

into one of the cloud-height categories and diurnally 

averaged. The cloud properties are also column averaged. The AVG also contains, for each region, the 

hourly average fluxes for the month and an overall 

monthly average.

 The Monthly Zonal and Global Radiative Fluxes 

and Clouds (ZAVG) product contains a month of 

space-and-time-averaged CERES data for a single 

scanner instrument. The ZAVG is also produced for 

combinations of scanner instruments. The space-andtime-averaged fluxes and cloud parameters on the 

CERES AVG product are spatially averaged from 1° 

regions to 1° zonal averages and a global monthly 

average. 

 A complete listing of parameters for these data 

products can be found in the CERES Data Products 

Catalog at asd-www.larc.nasa.gov/DPC/DPC.html.

Research and Applications

These products will be used by meteorological researchers to study climate and improve global climate 

models. Zonal quantities are needed for studying energy transport since averaging on large spatial scales 

minimizes the effects of regional-scale anomalies in 

studying climate change and global dynamics. Global 

averages can be compared with other historical data 

sets derived from different regional scales to detect 

climate temperature trends and evaluate large-scale 

climate anomalies such as the effects of major volcanic eruptions.

Data Set Evolution

The product algorithm first calculates the means on a 

regional (1° equal-angle grid) basis from one month 

of synoptic maps from the SYN 3-hourly product. 

Regional means are then combined to obtain zonal 

and global averages. The main steps of the monthly 

averaging process are: 1) regionally sort the synoptically ordered data, 2) linearly average all flux data 

to produce monthly and monthly-hourly means, 

3) average the cloud properties using the proper 

weighting schemes, and 4) combine and average the 

regional means into zonal and global means. Once 

regional means are computed for all parameters and 

all regions, these means are combined into zonal and 

global means. Area-weighting factors are used to correct for the variation of grid-box size with latitude. 

 The analysis algorithms are described in CERES ATBD Subsystem 8.0, which can be found at 

eospso.gsfc.nasa.gov/atbd/cerestables.html.

Suggested Reading

Harrison, E. F. et al., 1990b.

Li, Z., and H. Leighton, 1993.

Wielicki, B. A. et al., 1998.

Young, D. F. et al., 1995b.
116 Data Products Handbook - Volume 1 • Cloud and Aerosol Properties and Radiative Energy Fluxes 

CERES Monthly Gridded TOA/

Surface Fluxes and Clouds 

(SFC)

Product Description

The Monthly Gridded TOA/Surface Fluxes and 

Clouds (SFC) product contains a month of spaceand-time averaged CERES data for a single scanner 

instrument. The SFC is also produced for combinations of scanner instruments. All instantaneous 

shortwave, longwave, and window fluxes at the 

top of the atmosphere (TOA) and surface from the 

CERES SSF product for a month are sorted by 1° 

spatial regions, by day number, and by the local hour 

of observation. The mean of the instantaneous fluxes 

for a given region-day-hour bin is determined and 

recorded in the SFC product along with other flux 

statistics and scene information. These average fluxes 

are given for both clear-sky and total-sky scenes. The 

regional cloud properties are column averaged and 

are included in the SFC product.

 A complete listing of parameters for this data 

product can be found in the CERES Data Products 

Catalog at asd-www.larc.nasa.gov/DPC/DPC.html.

Research and Applications

This product provides a 1° gridded version of the 

instantaneous footprint SSF data. The data volume 

is an order of magnitude smaller, but it retains the 

ability to compare to weather or climate model results at 1° resolution. It can also serve as a smaller 

browse product. This data set is a primary input to 

the SRBAVG data product which merges the CERES 

data with 3 hourly geostationary data to provide more 

accurate diurnal cycles and time averages. Care is 

taken to let the geostationary data provide the relative 

diurnal cycle magnitude, but for CERES to provide 

the absolute accuracy of the fluxes. Users who need 

within atmosphere fluxes or the most consistent set 

of surface to atmosphere to TOA fluxes should use 

the FSW, SYN, and AVG CERES data products. Validated data for SRBAVG will be available earlier than 

the more complete AVG product. Finally, the AVG 

product uses GMT defined day boundaries, while the 

SRBAVG product uses local solar time. 

TRMM and Terra / CERES

CERES AVG and ZAVG Products Summary

Coverage: Global

Spatial/Temporal Characteristics: 1° region, 

1° zone, global/month

Key Geophysical Parameters: Averaged clearsky, total-sky surface, TOA, and standard CERES pressure-level fluxes; cloud parameters 

include: cloud overlap, pressure, temperature, 

optical depth, ice-particle properties, liquidwater properties, PAR

Processing Level: 3

Product Type: Standard, post-launch

Maximum File Size: 1500 MB

File Frequency: 3/month

Primary Data Format: HDF

Browse Available: No

Additional Product Information: 

eosweb.larc.nasa.gov/PRODOCS/ceres/

table_ceres.html

DAAC: Langley Atmospheric Sciences Data 

Center, eosweb.larc.nasa.gov

Science Team Contact: T. Wong
Cloud and Aerosol Properties and Radiative Energy Fluxes • Data Products Handbook - Volume 1 117

Data Set Evolution

The parameters generated for this product are derived 

from the SSF product, which contains all the flux 

and cloud parameters. Each of the parameters from 

SSF is averaged to the 1° grid using equal weighting, 

which results in approximately 50 footprints being 

averaged for each geographic cell. These parameters 

are all inputs to the SRBAVG final radiation-budget 

product.

 The analysis algorithms are described in CERES ATBD Subsystem 4.6, which can be found at 

eospso.gsfc.nasa.gov/atbd/cerestables.html.

Suggested Reading

Barkstrom, B. R., and B. A. Wielicki, 1995.

Cess, R. et al., 1991.

Gupta, S. K. et al., 1992.

Gupta, S. K. et al., 1995.

Inamdar, A. K., and V. Ramanathan, 1994.

Inamdar, A. K., and V. Ramanathan, 1995.

Kratz, D. P., and Z. Li, 1995.

Li, Z. et al., 1993.

Wielicki, B. A. et al., 1998.

TRMM and Terra / CERES

CERES Monthly Gridded TOA/Surface 

Fluxes and Clouds Summary

Coverage: Global

Spatial/Temporal Characteristics: 1° region/

hour

Key Geophysical Parameters: Total-sky, 

clear-sky, and angular-model scene radiative 

fluxes (TOA and surface), cloud parameters, 

including cloud pressure, cloud altitude, cloud 

temperature, optical depth

Processing Level: 3

Product Type: Standard, post-launch

Maximum File Size: 615 MB

File Frequency: 18/month

Primary Data Format: HDF

Browse Available: No

Additional Product Information: 

eosweb.larc.nasa.gov/PRODOCS/ceres/table_

ceres.html

DAAC: Langley Atmospheric Sciences Data 

Center, eosweb.larc.nasa.gov

Science Team Contacts: D. F. Young, 

D. P. Kratz

Downward shortwave flux at the surface for all-sky conditions. The results are from TRMM data for Feb 03, 1998. Gaps between orbit 

tracks show the limit of the VIRS imager scan swath. The SFC data product contains 1o gridded data for TOA and surface fluxes along 

with matching surface, aerosol, and cloud data for each grid box. Surface flux algorithms are simpler than those in the CRS and FSW 

products. On the internet, visit visibleearth.nasa.gov to view a variety of CERES data images.
118 Data Products Handbook - Volume 1 TRMM and Terra / CERES • Cloud and Aerosol Properties and Radiative Energy Fluxes 

dependence of albedo on solar zenith angle based 

on the SSF data, and improved diurnal sampling 

achieved using the 3-hourly geostationary data for 

the relative diurnal shape while the CERES data 

defines the absolute accuracy and broadband direct 

measurements. SRBAVG is also advanced over 

ERBE ES-4 and ES-9 in that it contains cloud and 

aerosol properties, as well as surface radiative flux 

estimates derived using simple efficient parameterizations. This product should be broadly useful for 

climate and weather model verification, as well as for 

studying the role of clouds and aerosols in climate 

change. Users who need atmospheric fluxes or more 

rigorously constrained surface flux estimates should 

use the AVG gridded product.

Data Set Evolution

This product is obtained from the chain of processes which start with the 1B radiances and flow 

through products BDS, SSF, SFC, and the CERES 

Instrument Earth Scans (IES), ultimately producing 

the final averaged radiative flux and surface-radiation-budget (SRB) product. The regional fluxes are 

calculated using two different methods, which will 

help produce very stable, accurate long-term data 

sets and averages for use in detailed regional studies 

of radiation and clouds and interdisciplinary studies. This is a post-launch product. Currently, there 

is no adopted method available for producing totalsky surface LW flux from TOA flux. Data required 

for parameterization must be obtained from either 

Meteorological Ozone and Aerosol (MOA) atmoCERES Monthly TOA/Surface 

Averages (SRBAVG)

Product Description

The Monthly TOA/Surface Averages (SRBAVG) 

product contains a month of space-and-time-averaged CERES data for a single scanner instrument. 

The SRBAVG is also produced for combinations of 

scanner instruments. The monthly average regional 

flux is estimated using diurnal models and the 1° 

regional fluxes at the hour of observation from the 

CERES SFC product. A second set of monthly average fluxes is estimated using concurrent diurnal information from geostationary satellites. These fluxes 

are given for both clear-sky and total-sky scenes and 

are spatially averaged from 1° regions to 1° zonal 

averages and a global average. For each region, the 

SRBAVG also contains hourly average fluxes for the 

month and an overall monthly average. The cloud 

properties from SFC are column averaged and are 

included on the SRBAVG.

 A complete listing of parameters for this data 

product can be found in the CERES Data Products 

Catalog at asd-www.larc.nasa.gov/DPC/DPC.html.

Research and Applications

SRBAVG provides the next generation of TOA fluxes 

beyond the ERBE capability. Improvements include 

the advanced angular dependence models used in 

the SSF input TOA fluxes, advanced models of the 

30 120 210 300 390

Surface Downward Shortwave Flux (Watts per square meter)

February 1998 monthly average downward shortwave flux at the surface for all-sky conditions. The data are from TRMM and 

is restricted to 40N to 40S by the TRMM spacecraft orbit. Surface SW downward fluxes decrease as cloud cover and cloud optical thickness increase. The figure for February 1998 is near the peak of the large 1997/98 El Niño. The dark band south of the 

equator shows the anomalous continuous band of convective cloud across the entire Pacific and Indian Oceans. On the internet, 

visit visibleearth.nasa.gov to view a variety of CERES data images.
Cloud and Aerosol Properties and Radiative Energy Fluxes • Data Products Handbook - Volume 1 119

spheric data sets or from CERES and International 

Satellite Cloud Climatology Project (ISCCP) cloud 

properties. In addition, data used in this product are 

limited to days in which there is at least one observation. The cloud parameters are also averaged 

monthly and monthly-daily. The surface radiation 

budget is the net radiative flux at the surface and is 

computed using the hourly gridded TOA and surface 

fluxes from SFC, parameterized models, atmospheric 

profiles, cloud parameters, and surface temperatures 

and emissivities to produce the downward and net 

shortwave and longwave fluxes at the surface. The 

LW algorithm is based on parameterized equations 

developed expressly for computing surface LW fluxes 

in terms of meteorological parameters obtained from 

satellite and/or other operational sources.

 The analysis algorithms are described in CERES 

ATBD Subsystem 10.0, which can be found at 

eospso.gsfc.nasa.gov/atbd/cerestables.html.

Suggested Reading

Cess, R. et al., 1991.

Gupta, S. K. et al., 1992.

Harrison, E. F. et al., 1995.

Li, Z. et al., 1993.

Suttles, J. T., and G. Ohring, 1986.

Wielicki, B. A. et al., 1998.

CERES Monthly TOA/Surface Averages 

Summary

Coverage: Global

Spatial/Temporal Characteristics: 1° region/

month

Key Geophysical Parameters: Averaged 

total-sky, radiative fluxes (TOA and surface), 

net surface radiative fluxes (surface radiation 

budget), averaged cloud parameters

Processing Level: 3

Product Type: Standard, post-launch

Maximum File Size: 1000 MB

File Frequency: 3/month

Primary Data Format: HDF

Browse Available: No

Additional Product Information: 

eosweb.larc.nasa.gov/PRODOCS/ceres/

table_ceres.html

DAAC: Langley Atmospheric Sciences Data 

Center, eosweb.larc.nasa.gov

Science Team Contact: D. F. Young

TRMM and Terra / CERES
120 Data Products Handbook - Volume 1 Terra / MISR • Cloud and Aerosol Properties and Radiative Energy Fluxes 

MISR Top-Of-Atmosphere 

(TOA)/Cloud Product (MIS04, 

MIS06, MIS07)

Product Description

The Level 2 Top-of-Atmosphere (TOA)/Cloud Product (MIS04) consists of TOA radiation and cloud information including: stereoscopically derived cloudtop altitudes at 1.1-km sampling with simultaneous 

cloud-tracked winds at 70.4-km sampling; Reflecting 

Level Reference Altitude (RLRA), texture indices, 

top-of-atmosphere (TOA) bidirectional reflectance 

factors and albedos at 2.2-km sampling; altitudebinned (high, middle, and low) and total cloud fractions at 17.6 km; and coarsely-sampled (35.2 km) 

TOA albedos. The coarse TOA albedos include a 

restrictive albedo, derived from the nine multiangle 

observations of a single region, and an expansive 

albedo, which is calculated including contributions 

from surrounding regions at the appropriate angles. 

Data from MIS04 are mapped to produce Level 3 

globally gridded products (MIS06, MIS07).

Research and Applications

The main purpose of the MISR TOA/Cloud Product 

is to produce accurate measures of spectral albedo 

and bidirectional reflectance classified by scene type. 

These enable study of the global effects of different 

types of cloud fields, including spatial and temporal 

dependencies, and determination of their effects on 

Earthʼs climate. Bidirectional reflectances of clear 

and cloudy regions obtained by MISR can be used 

to develop anisotropic reflectance models classified 

by cloud type, determine the spatial and temporal 

variability of cloud albedo, and validate coarse spatial resolution angular-reflectance models generated 

by other instruments. Automated stereo matching of 

multi-angle imaging is used to retrieve cloud-top 

elevations and cloud-tracked winds. MISR data can 

help to obtain a better understanding of the nonlinear 

scaling between sub-grid and grid-scale processes 

in GCMs and provide improved parameterization in 

these models.

MISR images of Hurricane Debby in the Atlantic Ocean, acquired on August 21, 2000, 

during Terra orbit 3600. The first panel on the left is the MISR downward-looking 

(nadir) view of the stormʼs eastern edge. The next two panels show results obtained 

using MISRʼs stereoscopic observations and an operational retrieval algorithm to 

retrieve cloud heights and winds. The highest wind speed measured is nearly 100 

kilometers/hour. On the internet, visit visibleearth.nasa.gov to view a variety of MISR 

data images.
Cloud and Aerosol Properties and Radiative Energy Fluxes • Data Products Handbook - Volume 1 121

Data Set Evolution

Earlier satellites, notably Nimbus-7 (ERB) and 

the NOAA-9 Earth Radiation Budget Experiment 

(ERBE), marked the beginning of techniques to 

directly integrate radiances from the same scene, 

measured more or less coincidentally at several 

different angles to yield the hemispherical flux. 

The Nimbus-7 scanner reduced its FOV to keep the 

viewed area about the same size, but had to look at 

different scenes across track and build up directional 

models. In its alongtrack mode, the NOAA-9 scanner obtained a very limited data set looking at fixed 

regions, but since its scanner had a fixed FOV the size 

of the target area changed systematically with angle. 

The presently available models have been painstakingly constructed from Nimbus-7 data, but cannot 

accommodate different cloud types, and define only 

a few categories of cloud cover amounts.

Suggested Reading

Di Girolamo, L., and R. Davies, 1994.

Horvath, A., and R. Davies, 2001.

Horvath, A., and R. Davies, 2001.

Loeb, N. G., and R. Davies, 1997.

Moroney, C. et al., 2002.

MISR TOA and Cloud Product Summary

Coverage: Daytime, 9-day for global repeat 

coverage

Spatial/Temporal Characteristics: 9-day for 

global coverage with 1.1 km, 2.2 km, 17.6 

km, 35.2 km sampling, and 70.4 km sampling 

(various parameters) at Level 2; monthly and 

seasonal globally gridded products at Level 3

Key Geophysical Parameters: TOA albedo, 

TOA bidirectional reflectance factor, cloud-top 

height, cloud-tracked wind, cloud screens, 

reflecting level reference altitude, altitudebinned cloud fraction

Processing Level: 2, 3

Product Type: Standard, at-launch (MIS04) 

post-launch gridded products (MIS06, 

MIS07)

Maximum File Size: 90 MB, (TOA/Cloud 

Stereo Parameters); 5 MB (TOA/Cloud Classifier Parameters); 370 MB (TOA/Cloud Albedo 

Parameters)

File Frequency: 15/day

Primary Data Format: HDF-EOS

Browse Available: No

Additional Product Information:

eosweb.larc.nasa.gov/PRODOCS/misr/table_

misr.html

DAAC: Langley Atmospheric Sciences Data 

Center, eosweb.larc.nasa.gov

Science Team Contact: R. Davies

Terra / MISR
122 Data Products Handbook - Volume 1 Terra / MISR • Cloud and Aerosol Properties and Radiative Energy Fluxes 

MISR Aerosol and Surface 

Product (MIS05, MIS08, 

MIS09)

Product Description

The Level 2 Aerosol/Surface Product (MIS05) 

contains a variety of information on the Earthʼs 

atmosphere and surface. The aerosol data include 

tropospheric aerosol optical depth on 17.6-km 

centers, archived with compositional model identifiers and retrieval residuals, ancillary data including 

atmospheric pressure and surface wind speed, and 

retrieval flags. The land surface data are mostly on 

1.1-km centers and include hemispherical directional 

reflectance factor, bihemispherical reflectance (i.e., 

albedo), bidirectional reflectance factor, directional 

hemispherical reflectance, BRF model parameters, 

albedo-based NDVI, Fraction of Photosynthetically 

Active Radiation (FPAR), and Leaf Area Index 

(LAI).

Research and Applications

MIS05, MIS08, and MIS09 products are used to study 

absorption and scattering in the lower atmosphere 

and at the surface for use in climate and biosphereatmosphere interaction studies. The MISR aerosol 

data sets enable: 1) the study of the global magnitude 

and variability of sunlight absorption and scattering 

by aerosols in the Earthʼs atmosphere (particularly 

tropospheric aerosols) and their effect on climate; 2) 

improved understanding of the sources, sinks, and 

global budgets of aerosols; and 3) improved atmospheric correction for surface imaging data acquired 

by MISR and other instruments (e.g., MODIS and 

ASTER).

 The MISR surface data sets enable: 1) the study 

of the global magnitude and variability of sunlight 

absorption and scattering by the Earthʼs surface, particularly through determination of the surface hemispherical reflectance (albedo); 2) improved measures 

of land surface classification and dynamics; and 3) 

improved observations of ocean color in the tropics 

by providing data in equatorial regions to supplement 

MODIS observations.

Data Set Evolution

MISR builds on earlier work with satellite and aircraft 

optical sensors such as the AVHRR and the Advanced 

Solid-state Array Spectroradiometer (ASAS). Surface 

hemispherical reflectance (spectral albedo) is retrievable with much higher accuracy from MISR multiangle observations than it can be inferred from solely 

nadir spectral reflectance factors. In addition, current 

aerosol retrievals (e.g., from AVHRR) are unable to 

distinguish different particle types, since they are 

based on measurements at a single wavelength and 

angle of view, and the algorithm to convert observed 

radiance to aerosol optical depth assumes particles 

of a fixed composition and size. MISR retrievals are 

based on more extensive coverage in both wavelength 

and view angle, providing greater ability to distinguish different particle types based on their physical 

and optical properties.

MISR images of the eastern United States acquired on March 6, 

2000, during Terra orbit 1155. The first panel is the downwardlooking (nadir) view, covering the region from Lake Ontario to 

northern Georgia, and spanning the Appalachian Mountains. 

The middle panel is the image taken by the forward-viewing 

70.5o camera. At this increased slant angle, the line-of-sight 

through the atmosphere is three times longer, and a thin haze 

over the Appalachians is significantly more apparent. The third 

panel shows the airborne particle (aerosol) amount, derived 

using MISRʼs operational aerosol retrieval algorithm. On the 

internet, visit visibleearth.gsfc.nasa.gov to view a variety of 

MISR data images.
Cloud and Aerosol Properties and Radiative Energy Fluxes • Data Products Handbook - Volume 1 123

Suggested Reading

Diner, D. J. et al., 2001.

Kahn, R. et al., 1997.

Kahn, R. et al., 1998.

Kahn, R. et al., 2001.

Knyazikhin, Y. et al., 1998.

Martonchik, J. V. et al., 1998.

Martonchik, J. V. et al., 2002.

MISR Aerosol and Surface Product 

Summary

Coverage: Daytime

Spatial/Temporal Characteristics: 9-day for 

global coverage with 1.1-km, 17.6-km, and 

70.4-km sampling (various parameters) at 

Level 2; 16-day, monthly, and seasonal globally gridded products at Level 3

Key Geophysical Parameters: Column aerosol 

optical depth, aerosol compositional model 

identifiers, directional hemispherical reflectance factors, hemispherical reflectances, 

BRF model parameters, NDVI, FPAR, LAI

Processing Level: 2, 3

Product Type: Standard, at-launch (MIS05); 

post-launch (MIS08, MIS09)

Maximum File Size: 40 MB (Aerosol Parameters); 500 MB (Surface Parameters)

File Frequency: 15/day

Primary Data Format: HDF-EOS Grid

Browse Available: No

Additional Product Information:

eosweb.larc.nasa.gov/PRODOCS/misr/table_

misr.html

DAAC: Langley Atmospheric Sciences Data 

Center, eosweb.larc.nasa.gov

Science Team Contact: J. Martonchik

MISR Aerosol Climatology 

Product (MIS12)

Product Description

The Aerosol Climatology Product (ACP)(MIS12) is 

generated at the MISR Scientific Computing Facility 

(SCF) and occasionally updated. It is used for interpretation of the aerosol data contained in MIS05. 

The ACP contains the physical and optical properties 

that define common atmospheric aerosol types. The 

parameters reported in the ACP include an aerosol 

model identifier (name, number, and composition); 

a water activity identifier (hygroscopic or not, and 

if so, how hydrophilic); a particle shape identifier 

(spherical, polyhedral, or irregular); a grid of relative 

humidity values for which all optical properties have 

been calculated; particle size distribution parameters; 

particle density (volume-weighted for mixtures); 

complex index of refraction; scattering and extinction cross-section; single scattering albedo; scattering anisotropy parameter; and phase function. It also 

includes the definitions of the aerosol mixtures used 

during generation of MIS 05.

Suggested Reading

Kahn, R. et al., 2001.

MISR Aerosol Climatology Product 

Summary

Coverage: N/A, one-time only, with possible 

infrequent updates

Resolution: N/A

Wavelengths: 446, 558, 672, and 866 nm; parameters include aerosol optical properties

Product Type: Internal

Maximum File Size: 0.2 MB 

File Frequency: N/A

Primary Data Format: HDF-EOS Grid

Browse Available: No

Additional Product Information:

eosweb.larc.nasa.gov/PRODOCS/misr/table_

misr.html

DAAC: Langley Atmospheric Sciences Data 

Center, eosweb.larc.nasa.gov

Science Team Contact: R. Kahn

Terra / MISR
124 Data Products Handbook - Volume 1 Terra / MODIS • Cloud and Aerosol Properties and Radiative Energy Fluxes 

retrieved are: the ratio between the two modes, the 

spectral optical thickness, and the mean particle 

size.

 The quality control of these products are based on 

comparison with ground stations and climatology.

 The related MODIS Aerosol Product ATBD, Algorithm for Remote Sensing of Tropospheric Aerosol 

from MODIS: Optical thickness over land and ocean 

and aerosol size distribution over the ocean, can be 

found in PDF format at eospso.gsfc.nasa.gov/atbd/

modistables.html.

Suggested Reading

Chu, D. A. et al., 1998.

Chu, D. A. et al., 2002.

Dubovik, O. et al., 2002.

Gao, B. C. et al., 2002b.

Holben, B. N. et al., 1998.

Ichoku, C. et al., 2002.

Kaufman, Y. J. et al., 1997a.

Kaufman, Y. J. et al., 1997b.

Kaufman, Y. J. et al., 2002.

King, M. D. et al., 1992.

King, M. D. et al., 1999.

King, M. D. et al., 2003.

Martins, J. V. et al., 2002.

Rao, C. R. N. et al., 1989.

Remer, L. A. et al., 1996.

Remer, L. A. et al., 2002.

Tanré, D. et al., 1997.

MODIS Aerosol Product 

(MOD04)

Product Description

The MODIS Aerosol Product (MOD04) monitors the ambient aerosol optical thickness over the 

oceans globally and over a portion of the continents. 

Further, the aerosol size distribution is derived over 

the oceans, and the aerosol type is derived over the 

continents. Daily Level 2 (MOD04) data are produced 

at the spatial resolution of 10 km, although made up 

from data at a resolution of 1 km (at nadir).

Research and Applications

Aerosols are one of the greatest sources of uncertainty in climate modeling. Aerosols modify cloud 

microphysics by acting as cloud condensation nuclei 

(CCN), and, as a result, impact cloud radiative properties and climate. Aerosols scatter back to space and 

absorb solar radiation. The MODIS aerosol product 

is used to study aerosol climatology, sources and 

sinks of specific aerosol types (e.g., sulfates and 

biomass-burning aerosol), interaction of aerosols 

with clouds, the hydrological cycle and atmospheric 

dynamics, and atmospheric corrections of remotely 

sensed surface reflectance over the land.

Data Set Evolution

Prior to MODIS, satellite aerosol measurements were 

limited to reflectance measurements in one (GOES, 

METEOSAT) or two (AVHRR) channels. There was 

no real attempt to retrieve aerosol content over land 

on a global scale. Algorithms had been developed for 

use only over dark vegetation. The blue channel on 

MODIS, not present on AVHRR, offers the possibility 

to extend the derivation of optical thickness over land 

to additional surfaces. The algorithms uses MODIS 

bands 1 through 7 and require prior cloud screening using MODIS data. Over the land, the dynamic 

aerosol models are derived from ground-based sky 

measurements and used in the net retrieval process.

 Over the ocean, three parameters that describe the 

aerosol loading and size distribution are retrieved. 

Pre-assumptions on the general structure of the size 

distribution are required in the inversion of MODIS 

data, and the volume-size distribution are described 

with two log-normal modes: a single mode to describe 

the accumulation mode particles (radius <0.5 μm) 

and a single coarse mode to describe dust and/or salt 

particles (radius >1.0 μm). The aerosol parameters 
Cloud and Aerosol Properties and Radiative Energy Fluxes • Data Products Handbook - Volume 1 125

MODIS Aerosol Product Summary

Coverage: Global over oceans, nearly 

global over land

Spatial/Temporal Characteristics: 10 km 

for Level 2

Key Science Applications: Aerosol 

climatology, biomass-burning aerosols, 

atmospheric corrections, cloud radiative properties, cloud and atmospheric 

dynamics, climate modeling

Key Geophysical Parameters: Atmospheric aerosol optical depth (global) and 

aerosol size distribution (oceans)

Processing Level: 2

Product Type: Standard, at-launch

Maximum File Size: 11.99 MB 

File Frequency: 144/day

Primary Data Format: HDF-EOS

Browse Available:

modis-atmos.gsfc.nasa.gov/MOD08_D3/

browse.html

Additional Product Information:

modis-atmos.gsfc.nasa.gov/MOD04_L2/

index.html

DAAC: Goddard Space Flight Center 

Earth Sciences DAAC, 

daac.gsfc.nasa.gov

Science Team Contacts: Y. J. Kaufman, 

D. Tanré, L. A. Remer

Terra / MODIS
126 Data Products Handbook - Volume 1 Terra / MODIS • Cloud and Aerosol Properties and Radiative Energy Fluxes 

1.1

1.0

0.8

0.6

0.4

0.2

0.0

1.0

0.9

0.8

0.7

0.5

0.4

0.3

Aerosol optical properties. The image at top depicts the aerosol optical thickness at 0.56 μm over the Eastern 

United States on May 4, 2001, showing pollution along the Ohio Valley and transport to the north Atlantic. 

The bottom image shows the ratio of aerosol optical thickness of the fine mode to the total aerosol optical 

thickness. On the internet, visit visibleearth.nasa.gov to view a variety of MODIS data images.
Cloud and Aerosol Properties and Radiative Energy Fluxes • Data Products Handbook - Volume 1 127

conducted by comparisons with stereo determinations 

from MISR and O2 A-band cloud heights from the 

Medium Resolution Imaging Spectrometer (MERIS) 

and lidar estimates as well as with two GOES determinations, aircraft lidar estimates plus observations of 

cirrus heights. Cloud emissivity will be compared to 

lidar-determined values. These interim products will 

be used in concert with field campaigns with the MAS 

instrument. The Menzel cloud-phase parameter will 

be validated using in situ data and by comparison to 

the King cloud-phase parameter.

 The King cloud-phase algorithm requires product 

MOD02, calibrated multispectral radiances. Cloudparticle size and optical thickness require these radiances plus the cloud-top parameters within MOD06. 

In addition, these parameters require NCEP or DAO 

analyses and profiles described above. The validation and quality control of these products will be 

performed primarily through the use of in situ measurements obtained during field campaigns and with 

the use of the MAS instrument.

 The related MODIS Cloud Product ATBDs, MODIS: Cloud Top Properties and Cloud Phase and 

Cloud Retrieval Algorithms for MODIS: Optical 

Thickness, Effective Particle Radius, and Thermodynamic Phase, can be found in PDF format at 

modis-atmos.gsfc.nasa.gov/reference_atbd.html.

Suggested Reading

Gao, B. C. et al., 1998.

Gao, B. C. et al., 2002a.

Gao, B. C. et al., 2003.

King, M. D. et al., 1992.

King, M. D. et al., 1996.

King, M. D. et al., 2003.

Nakajima, T. Y., and T. Nakajima, 1995.

Platnick, S. et al., 2000.

Platnick, S. et al., 2003.

Strabala, K. I. et al., 1994.

Wylie, D. P., and W. P. Menzel, 1998.

MODIS Cloud Product 

(MOD06)

Product Description

The MODIS Cloud Product (MOD06) combines 

infrared and visible techniques to determine both 

physical and radiative cloud properties. Daily global 

Level 2 (MOD06) data are provided. Cloud-particle 

phase (ice vs. water, clouds vs. snow), effective 

cloud-particle radius, and cloud optical thickness are 

derived using the MODIS visible and near-infrared 

channel radiances. Cloud-top temperature, height, 

effective emissivity, phase (ice vs. water, opaque 

vs. non-opaque), and cloud fraction are produced 

by the infrared retrieval methods both day and night 

at 5 × 5 1-km-pixel resolution. Finally, the MODIS 

Cloud Product includes the cirrus reflectance in the 

visible at the 1-km-pixel resolution, which is useful 

for removing cirrus scattering effects from the landsurface reflectance product.

Research and Applications

A thorough description of global cloudiness and 

its associated properties is essential to the MODIS 

mission for two reasons. First, clouds play a critical 

role in the radiative balance of the Earth and must be 

accurately described in order to assess climate and 

potential climate change accurately. In addition, the 

presence or absence of cloudiness must be accurately 

determined in order to retrieve properly many atmospheric and surface parameters. For many of these 

retrievals, cloud cover, even thin cirrus, represents 

contamination. Key radiative properties of clouds 

such as phase, optical thickness, and temperature 

may be retrieved using MODIS instruments with 

unprecedented resolution.

Data Set Evolution

The determination of cloud-top properties will require 

the use of MODIS bands 29 and 31-36, along with the 

cloud-mask product (MOD35), to screen for clouds. 

In addition, NCEP or Data Assimilation Office global 

model analyses of surface temperature and pressure, 

profiles of temperature and moisture, and blended 

SST analyses will be required in the calculation of 

cloud forcing as a function of atmospheric pressure 

and emissivity. The Menzel cloud-phase algorithm 

requires MODIS bands at 8.55 μm (band 29) and 

11.0 μm (band 31).

 Validation of MODIS cloud top pressure will be 

Terra / MODIS
128 Data Products Handbook - Volume 1 • Cloud and Aerosol Properties and Radiative Energy Fluxes 

Terra / MODIS

MODIS Cloud Product Summary

Coverage: Global

Spatial/Temporal Characteristics: Resolutions 

of 1 km or 5 km/once or twice per day (varies 

with parameter)

Key Science Applications: Cloud parameterization, climate modeling, climate monitoring, 

increasing accuracy of other MODIS retrievals

Key Geophysical Parameters: Cloud-particle 

phase (two algorithms), cloud-particle size 

and optical thickness, cirrus reflectance at 

0.66 μm, and cloud-top temperature, emissivity, and height

Processing Level: 2

Product Type: Standard, at-launch

Maximum File Size: 69.60 MB (daytime granules), 14.15 MB (nighttime granules) 

File Frequency: 288/day

Primary Data Format: HDF-EOS

Browse Available: 

modis-atmos.gsfc.nasa.gov/MOD08_D3/

browse.html

Additional Product Information:

modis-atmos.gsfc.nasa.gov/MOD06_L2/

index.html

DAAC: Goddard Space Flight Center Earth 

Sciences DAAC, daac.gsfc.nasa.gov

Science Team Contacts: M. D. King, 

S. Platnick, W. P. Menzel, B. C. Gao
Cloud and Aerosol Properties and Radiative Energy Fluxes • Data Products Handbook - Volume 1 129

Cloud properties over the western Pacific Ocean off the Kamchatka Peninsula on August 10, 2001. The image 

at top shows the cloud optical thickness derived from all cloudy pixels. The bottom image shows the cloud 

top pressure (in hPa) for all clouds in this scene. On the internet, visit visibleearth.nasa.gov to view a variety 

of MODIS data images.

>75.00

10

1

1000

883

766

650

539

416

300

Terra / MODIS
130 Data Products Handbook - Volume 1 Terra / MODIS • Cloud and Aerosol Properties and Radiative Energy Fluxes 

studies that help monitor variations in environmental 

conditions and aid research for assessing both natural 

and human-induced global change. 

Data Set Evolution

Definition of Time Spans

For the daily product (MOD08_D3), Level 2 granules 

that overlap any part of the data day (0000 to 2400 

UTC) are included in the computation of statistics. 

Therefore a particular Level 2 granule may be included in two consecutive MOD08_D3 products.

 The 8-day product (MOD08_E3) is computed by 

manipulating and summarizing the daily product 

over eight consecutive days. The running 8-day 

interval begins with the first day of MODIS data on 

Terra (February 25, 2000). The 8-day intervals for 

MODIS on Aqua will be the same 8-day intervals as 

for MODIS on Terra for the period of data overlap. 

It should be noted that the starting day of the 8-day 

interval is not reset to the 1st, during the rollover of 

a calendar month. However, during the rollover of a 

calendar year, the 8-day interval is reset to begin on 

January 1st. It should also be noted that the final 8-

day interval of any year includes 8 full days even if 

they extend into a new year; this rule will, in general, 

cause some overlap of the 8-day interval around every 

new year.

 The monthly product (MOD08_M3) is computed 

by manipulating and summarizing the daily product 

over a calendar month.

Definition of Daily Statistics

For the daily product, assume that x1, x2, ..., xn represent the retrieved pixel values of a Level 2 parameter 

over a 1° × 1° grid box, Qi is the quality flag for each 

retrieved pixel value, and wi is the weighting factor 

(1 for the daily case), then the simple statistics are 

defined as:

Xd = Σi wi • xi

 Σi wi

n

n

XdQ = Σi Qi • xi

 Σi Qi

n

n

Xstd = Σ1 (wi • xi

 – wi • Xd)

2

 Σi wi

2

n

n

XstdQ = Σ1 (Qi • xi

 – Qi • XdQ)

2

 Σi Qi

2

n

n

Xmin = min (x1, x2,..., xn)

Xmax = max (x1, x2,..., xn)

(1)

(2)

(3)

(4)

(5)

(6)

MODIS Level 3 Atmosphere 

Products (MOD08)

Product Description

There are three MODIS Level 3 Atmosphere Products, each covering a different temporal scale. The 

Earth Science Data Type names (and time spans) 

for each of the products are: MOD08_D3 (Daily), 

MOD08_E3 (8-Day), MOD08_M3 (Monthly).

 Each of these Level 3 products contains statistics 

derived from over 50 science parameters from the 

Level 2 Atmosphere products: MOD04_L2, MOD05_

L2, MOD06_L2, and MOD07_L2.

 A range of statistical summaries is computed, 

depending on the Level 2 science parameter being 

considered. Statistics for a given Level 2 measurement might include:

• Simple (mean, minimum, maximum, standard 

deviation) statistics 

• Parameters of normal and log-normal distributions 

• Fraction of pixels that satisfy some condition (e.g., 

cloudy, clear)

• Histograms of the quantity within each grid box 

• Histograms of the confidence placed in each measurement

• Histograms and/or regressions derived from comparing one science parameter to another; statistics 

may be computed for a subset that satisfies some 

condition 

• Pixel counts

Statistics are sorted into 1° × 1° cells on an equalangle grid and then summarized over the globe. The 

equal-angle grid has a fixed dimension of 360×180 

pixels.

 The daily product contains nearly 500 statistical 

summary parameters. The 8-day and monthly products, which are identical in format, include over 700 

statistical summary parameters.

 Additional information on the MODIS Level 

3 Atmosphere products can be obtained from 

the MODIS-Atmosphere web site at: modisatmos.gsfc.nasa.gov.

Research and Applications

Users should refer to the research applications of 

Level 2 Atmosphere products from MOD04, MOD05, 

MOD06, and MOD07 listed in this handbook. Level 

3 statistical products derived from Level 2 measurements lend themselves to longer-term time-series 
Cloud and Aerosol Properties and Radiative Energy Fluxes • Data Products Handbook - Volume 1 131

In these equations, (1) will be referred to as the “regular” mean, (2) as the QA-weighted mean, (3) as the 

regular standard deviation, (4) as the QA-weighted 

standard deviation, (5) as the minimum, and (6) as 

the maximum.

 The simple statistics also include daily log regular 

mean, log standard deviation, log QA mean, and log 

QA standard deviation. These log quantities are calculated as shown in equations (1-4), except that x1

, 

x2, ..., xn are replaced by their logarithms.

 Regression statistics, based on the pixels within 

each 1° × 1° grid cell, include the slope, intercept, 

mean squared error (MSE), and the coefficient of 

determination (R2).

 The histograms and joint histograms report the 

counts of the pixels falling into predetermined numerical intervals.

 The pixel counts are used to represent the number 

of pixels for the parameters that do not have QA flags, 

while the confidence-histograms-counts are used to 

represent the number of counts for each parameter 

that fall within each QA bin (e.g., good, very good, 

marginal, and total).

Definition of 8-day and Monthly Statistics

The 8-day and monthly statistics are based on the 

daily statistics with the assumption that the daily statistics from retrieved pixels can represent the statistics 

of the “populations” of each 1° × 1° grid cell. In other 

words, it is assumed that the samples composed of 

retrieved pixels can represent the “populations” composed of all the pixels within each grid cell.

 The simple statistics for 8-day and monthly quantities include mean, standard deviation, and minimum 

and maximum values of the corresponding daily 

means (i.e., daily mean, daily QA mean, daily log 

mean, and daily log QA mean).

 For example, if Xd1, Xd2, ...,Xdn are the daily means 

for a Level 3 parameter, then their monthly simple 

statistics (i.e., mean, standard deviation, minimum, 

and maximum) are represented by equations (1, 3, 5, 

and 6) with Xi replaced by Xdi. It should be noted that 

in the monthly case, the weights in equations (1) and 

(2) are taken either as unweighted (i.e., wi = 1), pixel 

weighted (e.g., weighted by the number of pixels over 

the total pixels), or fraction weighted (e.g., weighted 

by the cloud fraction).

 The fraction statistics represent the mean and 

standard deviation of the daily fraction values.

 The regression statistics, which include four components (slope, intercept, mean-squared-error, and 

the coefficient of determination), are calculated by 

using the daily mean values of the first parameter 

versus the daily mean values of the second parameter. 

Therefore, two daily mean time series are used to 

calculate monthly regression attributes.

 Histogram and joint-histogram statistics represent 

the number of times a parameter falls into each predefined interval.

 The total counts are reported. This is simply the 

summation of all the counts from each interval for 

the parameter.

 The 8-day and monthly product has an additional 

statistical parameter called “mean daily standard deviation.” This parameter is used to represent the mean 

values of the daily standard deviations, such as the 

mean of daily standard deviation, mean of daily QA 

standard deviation, mean of log standard deviation, 

and mean of log QA standard deviation. The standard 

deviation of the mean is used to describe the variation 

of a parameter around its monthly mean, while the 

mean of the standard deviation is used to describe the 

average daily variation of a parameter. For example, 

a parameter may have a large daily standard deviation on each day, but those variations may be similar 

to each other. In this case, the mean daily standard 

deviation will be large, but its standard deviation will 

be small. 

 The related MODIS data product ATBDs can be 

found in PDF format at modis-atmos.gsfc.nasa.gov/

reference_atbd.html.

Suggested Reading

Ackerman, S. A. et al., 1998.

Gao, B. C., and A. F. H. Goetz, 1990.

Gao, B. C. et al., 1993a.

Gao, B. C. et al., 1998.

Green, R. O., and J. E. Conel, 1995.

Gustafson, G. B. et al., 1994.

Hayden, C. M., 1988.

Kaufman, Y. J., and B. C. Gao, 1992.

Kaufman, Y. J., and L. A. Remer, 1994.

Kaufman, Y. J. et al., 1997a,b.

King, M. D. et al., 1992.

King, M.D. et al., 1996.

King, M. D. et al., 1998.

King, M. D. et al., 1999.

King, M. D. et al., 2003.

Kleepsies, T. J., and L. M. McMillan, 1984.

Nakajima, T. Y., and T. Nakajima, 1995.

Platnick, S. et al., 2000.

Prabhakara, C. et al., 1970.

Terra / MODIS
132 Data Products Handbook - Volume 1 • Cloud and Aerosol Properties and Radiative Energy Fluxes 

MODIS Level 3 Atmosphere Products 

Summary

 

Coverage: Global

Spatial/Temporal Characteristics: 1.0° latitudelongitude equal-angle grid/daily, 8-day, and 

monthly

Key Science Applications: Climate and 

ecosystem monitoring and modeling, cloud 

radiative properties, atmospheric properties, 

and atmospheric corrections

Key Geophysical Parameters: Aerosol properties, cloud radiative properties, atmospheric 

water vapor and temperature

Processing Level: 3

Product Type: Standard, at-launch

Maximum File Size: 474.77 MB (Daily 

Level 3), 883.22 MB (8-day and Monthly 

Level 3) 

File Frequency: 1/day (Daily Level 3), 1/8-day 

(8-Day Level 3), 1/month (Monthly

Level 3)

Primary Data Format: HDF-EOS

Browse Available: 

modis-atmos.gsfc.nasa.gov/ MOD08_D3/

browse.html (daily)

modis-atmos.gsfc.nasa.gov/MOD08_E3/

browse.html (8-day)

modis-atmos.gsfc.nasa.gov/MOD08_M3/

browse.html (monthly)

Additional Product Information:

modis-atmos.gsfc.nasa.gov/MOD08_D3 

(daily)

modis-atmos.gsfc.nasa.gov/MOD08_E3 (8-

day)

modis-atmos.gsfc.nasa.gov/MOD08_M3 

(monthly)

DAAC: Goddard Space Flight Center Earth 

Sciences DAAC, daac.gsfc.nasa.gov

Science Team Contact: M. D. King

Rao, C. R. N. et al., 1989.

Remer, L. A. et al., 1996.

Rossow, W. B., and L. C. Garder, 1993.

Saunders, R. W., and K. T. Kriebel, 1988.

Shapiro, M. A. et al., 1982.

Smith, W. L., and F. X. Zhou, 1982.

Smith, W. L. et al., 1985.

Stowe, L. L. et al., 1991.

Strabala, K. I. et al., 1994.

Sullivan, J. et al., 1993.

Tanré, D. et al., 1997.

Terra / MODIS
Cloud and Aerosol Properties and Radiative Energy Fluxes • Terra / MODIS Data Products Handbook - Volume 1 133

The images above depict Level 3 Atmosphere Monthly Mean for August 2001.

Figure A depicts Cloud Optical Thickness for combined water, ice, and undetermined phase clouds. Optical thickness, a unit-less 

parameter, is a measure of the attenuation of solar radiation through a column of atmosphere. Thicker, more opaque clouds (white) 

can be clearly seen over the inter-tropical convergence zone (ITCZ) and roaring 40ʼs (latitude).

Figure B depicts Aerosol Optical Depth (AOD). Aerosols, one of the greatest sources of uncertainty in climate modeling, vary in time 

in space and can lead to variations in cloud microphysics, which could impact cloud radiative properties and climate. Large scale 

transport of dust, pollution and/or smoke can be detected moving west over the Atlantic from the Sahara; and east over the Pacific from 

China. Corresponding retrieved size parameters (not shown) can help identify aerosol type. It should be noted that AOD algorithms 

were developed for use over dark vegetation and ocean; leaving gaps in the image clearly visible over the (bright) deserts.

Figure C depicts Total Atmospheric Column Water Vapor in centimeters. Also called total column precipitable water, this parameter 

is derived from a near-infrared algorithm. It should be noted that the near-infrared is very sensitive to boundary-layer water vapor 

since it is derived from attenuation of reflected solar light from the surface. This product is essential to understanding the hydrological 

cycle, aerosol properties, aerosol-cloud interactions, energy budget, and climate. On the internet, visit visibleearth.nasa.gov to view a 

variety of MODIS data images.

August 2001

70.0

10.0

1.0

A

B

C

August 2001

0.800

0.600

0.400

0.200

0.000

August 2001

7.500

5.625

3.750

1.875

0.000
134 Data Products Handbook - Volume 1 • Cloud and Aerosol Properties and Radiative Energy Fluxes 

 Cloud-mask validation has been conducted using 

MAS data from several field campaigns, radar and 

lidar observations from the SGP CART site, and comparisons with cloud products from NOAA operational 

instruments. 

 The related MODIS Cloud Mask ATBD, Discriminating Clear Sky from Cloud with MODIS, can be 

found in PDF format at modis.gsfc.nasa.gov/data/

atbd/atbd_mod06.pdf.

MODIS Cloud Mask Summary

Coverage: Global

Spatial/Temporal Characteristics: 250 m and 

1 km/daily

Key Science Applications: Cloud determination and screening, climate modeling, climate 

monitoring, increasing accuracy of other 

MODIS retrievals

Key Geophysical Parameters: Presence of 

cloud

Processing Level: 2

Product Type: Standard, at-launch

Maximum File Size: 47.42 MB 

File Frequency: 288/day

Primary Data Format: HDF-EOS

Additional Product Information:

modis-atmos.gsfc.nasa.gov/MOD35_L2/

index.html

DAAC: Goddard Space Flight Center Earth 

Sciences DAAC, daac.gsfc.nasa.gov

Science Team Contacts: W. P. Menzel, 

S. A. Ackerman

Terra / MODIS

MODIS Cloud Mask (MOD35)

Product Description

The MODIS Cloud Mask product (MOD35) is a daily, 

global Level 2 product generated at 1-km and 250-m 

(at nadir) spatial resolutions. The algorithm employs 

a series of visible and infrared threshold and consistency tests to specify confidence that an unobstructed 

view of the Earthʼs surface is observed. The 250-m 

cloud-mask flags are based on visible channel data but 

use 1-km cloud mask results as ancillary information. 

Radiometrically accurate radiances are required, so 

holes in the Cloud Mask will appear wherever the 

input radiances are incomplete or of poor quality.

Research and Applications

A determination of the presence of global cloudiness 

is essential to the MODIS mission for two reasons. 

First, clouds play a critical role in the radiative balance of the Earth and must be accurately described to 

assess climate and potential climate change. Second, 

the presence of cloudiness must be accurately determined to retrieve properly many atmospheric and 

surface parameters. For many of these retrieval algorithms even thin cirrus represents contamination.

Data Set Evolution

The MODIS cloud-mask algorithm employs a battery of spectral tests, which use methodology applied developed for the AVHRR Processing scheme 

Over cLoud Land and Ocean (APOLLO), International Satellite Cloud Climatology Project (ISCCP), 

CLoud Advanced Very high resolution Radiometer 

(CLAVR), and Support of Environmental Requirements for Cloud Analysis and Archive (SERCAA) 

to identify cloudy FOVs. From these a clear-sky 

confidence level (high confident clear, probably 

clear, undecided, cloudy) is assigned to each FOV. 

For inconclusive results, spatial- and temporal-variability tests are applied. The spectral tests rely on 

radiance (temperature) thresholds in the infrared and 

reflectance thresholds in the visible and near-infrared. Thresholds vary with surface type, atmospheric 

conditions (moisture, aerosol, etc.), and viewing 

geometry. Along with MOD02 calibrated radiances, 

a 1-km land/water mask, DEM, ecosystem analysis, 

and snow/ice cover maps are required as inputs.
Cloud and Aerosol Properties and Radiative Energy Fluxes • Data Products Handbook - Volume 1 135

Conf. 

Clear

Probably 

Clear

Probably

Cloudy

Cloudy

These images depict L1B (left) and Unobstructed FOV Cloud Mask (right) from August 1, 2002. Visible image and corresponding cloud mask for a scene over eastern Australia. A determination of the presence of global cloudiness is essential 

to the MODIS mission for two reasons. First, clouds play a critical role in the radiative balance of the Earth and must be 

accurately described to assess climate and potential climate change. Second, the presence of cloudiness must be accurately 

determined to properly retrieve many atmospheric and surface parameters. For many of these retrieval algorithms even thin 

cirrus represents contamination. On the internet, visit visibleearth.nasa.gov to view a variety of MODIS data images.

Terra / MODIS

Suggested Reading

Ackerman, S. A. et al., 1998.

Gao, B. C. et al., 1993a.

Gao, B. C. et al., 2002a,b.

Gao B. C. et al., 2003.

Gao, B. C. and Y. J. Kaufman, 2003.

Gustafson, G. B. et al., 1994.

King, M. D. et al., 1998.

King, M. D. et al., 2003.

Platnick, S. et al., 2003.

Rossow, W. B., and L. C. Garder, 1993.

Saunders, R. W., and K. T. Kriebel, 1988.

Stowe, L. L. et al., 1991.

137

Chapter 7

Lightning

TRMM

LIS
138 Data Products Handbook - Volume 1 Overview • Lightning 

Lightning – An Overview

field of view (FOV) is sufficient to observe a point 

on the Earth or a cloud for 83 seconds, adequate to 

estimate the flashing rate of many storms. This makes 

it possible to estimate lightning frequency even for 

storms with flash rates as low as 1-2 discharges per 

minute.

Product Interdependency and 

Continuity with Heritage Data

During the 1980s, extensive optical and electrical 

observations of lightning were made from high-altitude NASA U-2 aircraft with the primary objective 

of defining a baseline design criterion for a space 

sensor capable of mapping lightning discharges from 

geostationary orbit during day and night, with high 

spatial resolution and high detection efficiency. The 

results, combined with parametric trade-off studies 

and other research, have clearly established the feasibility of making this kind of lightning measurement 

from space with present state-of-the-art technology.

 Global lightning signatures from the Defense 

Meteorological Satellite Program (DMSP) Optical Linescan System (OLS) have been analyzed 

from the filmstrip imagery and digital DMSP tapes 

archived at the NOAA National Geophysical Data 

Center (NGDC) National Snow and Ice Data Center 

in Boulder, Colorado. This global lightning database 

is a useful reference data set for LIS.

 Prior to, and during the TRMM mission, spacebased lightning observations had been provided by 

the Optical Transient Detector (OTD), launched in 

April 1995 as a secondary payload on a Pegasus 

launch vehicle. The OTD mission ended in April 

2000.

 In a near-polar orbit, OTD detected all types 

of lightning events, including cloud-to-ground, 

cloud-to-cloud, and intra-cloud. Considered to be 

a LIS prototype, OTD data populated a database of 

global lightning activity to which LIS data are being 

added.

Relationship to Global Change

Understanding lightning is a way to understand the 

interplay of some key components of atmospheric 

dynamics. Lightning is closely coupled to storm 

convection dynamics and can be correlated to the 

global rates, amounts, and distribution of convective 

precipitation. It points to where strong convection is 

occurring, when large quantities of water are growing 

in the mixed phase regions of storms, and suggests 

how latent heat is being released during the stormʼs 

life cycle. Since the microscales on which particles 

interact to generate electricity are coupled through 

storm-scale processes to synoptic-scale systems, 

lightning activity should provide information on the 

development of the atmosphere over many scale sizes, 

especially in the tropics. 

 The Lightning Imaging Sensor (LIS) on the TRMM 

Observatory is designed to detect, locate, and measure 

the intensity of lightning for scientific investigation 

of the distribution and variability of total lightning 

over the Earth and to increase our understanding 

of the Earthʼs atmosphere system. LIS provides a 

global lightning and thunderstorm climatology from 

which changes (even subtle temperature variations) 

might be detected. These data are used in a variety 

of global change studies, including cloud characterization, hydrologic cycle studies, storm convection, 

microphysics and dynamics, and the seasonal and 

interannual variability of thunderstorms.

 LIS represents a significant advance over any previous satellite-borne lightning detectors. Those instruments could provide only a relative global distribution 

of lightning of a strictly statistical, and often biased, 

nature. Additionally, they required collecting data for 

long periods of time before they could be meaningfully interpreted in terms of global distributions. It 

was not possible to compare the lightning frequency 

of a particular storm with the earlier estimates. Lightning observations from LIS can be readily associated 

with the thunderstorms that produce them, and the 

detection of even a single discharge is significant and 

provides important information (e.g., storm location, 

precipitation estimates, storm height, the presence of 

ice, lightning frequency, electrical output, etc.).

 LIS has been designed to study the distribution and 

variability of total lightning on a global basis. Its staring imager is optimized to locate and detect lightning 

with storm-scale resolution of 5-10 km over a large 

region (600 × 600 km) of the Earthʼs surface. The 
Lightning • Overview Data Products Handbook - Volume 1 139

Suggested Reading

Boccippio, D. J. et al., 1998.

Boccippio, D. J. et al., 2000.

Boccippio, D. J. et al., 2000a,b.

Boccippio, D. J. et al., 2002.

Buechler, D. E. et al., 2000.

Christian, H. J. et al., 1999.

Christian, H. J. et al., 2000.

Christian, H. J. et al., 2003.

In this image, lightning flashes (bright white spots)—recorded during the NASA 

Lightning Imaging Sensorʼs pass over storms moving through Oklahoma in October 

1998—are overlaid on high-resolution infrared cloud-top data from the orbiting 

Tropical Rainfall Measuring Mission (TRMM) satellite. High flash rates visible in the 

heaviest storm areas may indicate the potential for tornado formation. With an optical 

sensor locked into geostationary orbit over the Earth, such useful information would 

be available continuously to meteorologists and other professional stormwatchers. On 

the internet, visit visibleearth.nasa.gov to view a variety of LIS data images.

Del Genio, A. D. et. al., 2002.

Goodman, S. J. et al., 2000.

Koshak, W. J. et al., 2000.

Kummerow, C. et al., 1998.

Kummerow, C. et al., 2000.

Peterson, W. A. et al., 2001.

Thomas, R. J. et al., 2000.

Toracinta, R. E. et al., 2002.
140 Data Products Handbook - Volume 1 • Lightning 

 Granules are organized structurally into orbit summary, point data, and continuous (one-second) data 

records. Point data include the Level 1 view-time 

data and all lightning data (Level 1 events and Level 

2 groups, flashes, and areas). One-second data include 

both Level 1 navigation and diagnostic data and Level 

3 vector data.

Research and Applications

LIS views a total area exceeding 600 km × 600 km 

at the cloud top using a 128 × 128 photodiode array. 

Individual storms and storm systems are monitored 

for 83 seconds, a period long enough to obtain a measure of the lightning flash rate while the storm is in 

the field of view of the sensor. This makes it possible 

to estimate lightning frequency even for storms with 

flash rates as low as 1-2 discharges per minute. LIS 

represents a significant advance over any previous 

satellite-borne lightning detector. Lightning observations from LIS can be readily associated with the 

thunderstorms that produce them, and the detection of 

even a single discharge is significant and provides important information (e.g., storm location, precipitation 

estimates, storm height, the presence of ice, lightning 

frequency, electrical output, etc.). The earlier satelliteborne lightning instruments provided only a relative 

global distribution of lightning of a strictly statistical 

nature; this required collecting data for long periods 

of time before it could be meaningfully interpreted 

in terms of global distributions. It was not possible 

to determine the lightning frequency of a particular 

storm as compared with the earlier estimates.

Data Set Evolution

NASA U-2 aircraft lightning detection data were 

collected in order to provide a baseline for future 

satellite-based lightning instruments. Based on a 

combination of the results of U-2 data and advances 

in technology, the OTD has been able to begin establishing a database of lightning observations from 

a near-polar orbit at a 70° inclination, at an altitude 

of 740 km, and with a FOV of 1300 km × 1300 km. 

LIS data sets will further build upon OTD with a 

FOV of 600 km × 600 km and a storm-scale resolution of 5-10 km.

TRMM / LIS

Lightning Imaging Sensor 

Product (LIS02-LIS18)

Product Description

TRMM LIS products of Level 1 and higher are contained in data granules corresponding to individual 

orbits; segmented at the southernmost nadir point 

of each orbit. Level 1, 2, and 3 data are included in 

each granule. Level 2 and higher products primarily 

consist of aggregation of the primary LIS observable 

(CCD pixel events) into variables corresponding to 

lightning strokes, flashes, and thunderstorm cells. 

Hierarchical links between the levels are included, 

as well as all data required to recompute Level 2 

and 3 products from Level 1 data using alternative 

algorithms. Data subelements are organized functionally in orbit granules, rather than by processing 

level (i.e., some data containers may contain more 

than one level of data).

 Level 1 data include events (pixel amplitudes 

which exceed a threshold for 2 ms), viewtime granules (discretized volumes of x, y, t sampling space 

denoting actual LIS viewing), navigation data (transform matrices needed to geolocate individual pixel 

events) and diagnostic data (calibration tables and 

warning or fatal error flags for a variety of instrument, 

platform, environmental and processing algorithm 

conditions). Pixel events are tagged with CCD and 

geolocated location, amplitude, calibrated radiance, 

time, applied threshold, and other parameters. 

Viewtime granules include location on a fixed 0.5° 

× 0.5° registration grid, start and end viewing times, 

and adjustments for partial grazing by the LIS FOV. 

Navigation and diagnostic data are reported every 

one second.

 Level 2 data include groups (adjacent pixel events 

that are concurrent in time, corresponding to lightning 

strokes), flashes (spatially and temporally proximate 

groups, corresponding to true lightning flashes) and 

areas (spatially proximate flashes, intended to isolate 

thunderstorm cells from lightning-only data). Each 

of these products includes links to its subcomponents 

and its parent under the aggregation algorithms, and 

includes derived parameters (e.g., net calibrated 

radiance, total spatial footprint, automated quality/

confidence flags, etc.).

 Level 3 data include raster browse images and summary vector data, the latter reported in one second 

intervals and organized in the same data record as 

navigation and diagnostic data. Level 1 and 2 data 

are used to produce equal-angle maps of lightning 

activity, controlled for total and diurnal sampling and 

cross-calibrated with antecedent and concurrent OTD 

data.
Lightning • TRMM / LIS Data Products Handbook - Volume 1 141

LIS Product Summary

Coverage: Global, 35°N to 35°S

Spatial/Temporal Characteristics: 4 km × 4 km 

pixel at nadir, orbit granules, 2 ms resolution; 

2.5 × 2.5 monthly, 47-day moving average

Key Science Applications: Thunderstorm distribution and variability, rainfall mapping and 

thunderstorm analysis

Key Geophysical Parameters: Lightning 

events, groups, flashes, areas

Processing Level 1B Products:

LIS02 – Image Attributes

LIS03 – Events Statistics

LIS04 – Group Statistics

Processing Level 2 Products:

LIS05 – Flash Statistics

LIS06 – Area Statistics

LIS07 – Flash Density

LIS08 – 2.5° × 2.5° Equal Angle Monthly 

 Grid

LIS09 – 500 km × 500 km Equal Area 

 Monthly Grid

Processing Level 3 Products:

LIS10 – Orbit Attributes

LIS11 – Threshold Attributes

LIS12 – Browse Area

LIS13 – Vector Statistics

LIS14 – Metadata Description

LIS15 – Summary Data

LIS16 – Flash Rate Data

LIS17 – Ephemeris

LIS18 – Event Rate Sets

Product Type: Standard, at-launch

Maximum File Size:

Level 1B Background Data - 5.54 MB

Level 2 Science Data - 1.93 MB

Level 3 Browse and Summary Data - .04 MB

File Frequency: 1/orbit

Primary Data Format: HDF-EOS

Browse Available: Yes (GIF format)

Additional Product Information:

thunder.nsstc.nasa.gov/data/

DAAC: Global Hydrology Resource Center, 

ghrc.msfc.nasa.gov

Science Team Contacts: H. Christian, 

S. Goodman, D. Boccippio
143

Chapter 8

Atmospheric

Chemistry

Terra

 MOPITT
144 Data Products Handbook - Volume 1 • Atmospheric Chemistry 

Atmospheric Chemistry – An Overview

Relationship to Global Change Issues disciplines (e.g., ocean color, biomass burning, and 

land vegetation). Data products associated with those 

discipline areas are described in other sections of this 

document. Finally, knowledge is required of the regional increases in tropospheric ozone as well as the 

decrease in stratospheric ozone.

Product Overview

Key to our understanding of the horizontal and 

vertical measurements of the troposphere is the 

measurement of CO and CH4. 

 Carbon monoxide possesses a lifetime, on the order 

of two months, such that the CO is moved around 

on a regional scale, and thus performs the function 

of a chemical tracer. In addition to horizontal transport, studies have shown that there is considerable 

vertical transport of CO, pointing to the use of 3-D 

measurements, which could be used to study chemical 

transport processes in the troposphere. MOPITT CO 

data, along with data from other instruments, are used 

in modeling efforts by several researchers to advance 

our understanding of global tropospheric chemistry 

and its relationship to sources, sinks, and atmospheric 

transports.

 The full range of the effects of the increased concentration of CO are not fully understood at present, 

but it is believed that CO is photochemically active 

and plays a major role in the concentration of OH 

radicals in the troposphere. The OH radical acts as 

a “cleansing agent” in the troposphere, oxidizing 

many compounds. Satellite observations may clarify 

whether CO is increasing or decreasing. Increases 

in CO are likely to be the result of a rise in human 

activities. Increases in CO may deplete tropospheric 

radicals, thereby reducing the yearly removal of many 

natural and anthropogenic trace species. In particular, 

this effect may add to the increase of CH4, which in 

turn could further reduce OH concentration. Increased 

CO may also indirectly intensify global warming and 

perturb stratospheric ozone by increasing the lifetime 

of other trace greenhouse gases. Greenhouse gases are 

those chemicals that permit the influx of solar radiation to the surface below, but inhibit to some degree 

longwave radiation leaving the Earth, producing a 

net warming effect.

 The MOPITT data set also addresses the measurement of CH4

. Methane is the most abundant 

hydrocarbon in the atmosphere and possesses 

several infrared absorption bands, including one at 

approximately 7.7 μm, which lies in an atmospheric 

Overview

In recent years, increasing concern has focused on the 

natural and anthropogenic alteration of the Earthʼs 

environment and how these activities impact global 

change. Human activities are changing the concentrations of trace constituents in the troposphere; 

these in turn affect the radiative balance, dynamics, 

and chemistry of the atmosphere. One of the most 

pressing global change issues facing researchers 

and policymakers today involves the greenhouse effect and potential global warming. Carbon dioxide, 

nitrous oxide, methane, and chlorofluorocarbons 

(CFCs) are the principal anthropogenic greenhouse 

gases that are responsible for this effect. Of these, 

carbon dioxide has the highest concentration and 

plays the most significant role. However, the other 

gases are more efficient (on a per molecule basis) 

at trapping infrared radiation, and their combined 

effects are almost as large as that of carbon dioxide, 

even though their concentrations are much smaller. 

Ozone also proves significant, because the catalytic 

chemical process involved in its breakdown in the 

stratosphere and its increasing tropospheric concentrations have a multifaceted influence on the Earthʼs 

radiative balance.

 Within the Terra timeframe, the primary objective 

of studies in tropospheric chemistry is understanding 

the interaction with the surface/ocean/biomass systems, atmospheric transports, and the carbon cycle. 

The particular focus is the time evolution of the distributions of carbon monoxide (CO) and methane (CH4

) 

in the troposphere. The scientific questions become 

convoluted when considering the sources, chemistry, 

and ultimate sinks of minor atmospheric constituents 

and their interrelationships. The planetʼs inorganic 

and organic matter are the sources of many of these 

gases, increasing the importance of land-surface studies to determine emission rates and locations. Yet, the 

large-scale and seasonal distribution of these sources 

remains poorly understood. Space-based remote sensing provides the ideal platform for global monitoring 

of trace gas sources.

 Whether the changes are anthropogenic or naturally 

occurring, understanding these changes in the trace 

chemical makeup of the troposphere is a critical 

component in anticipating global climate change. 

On Terra, the Measurements of Pollution in The 

Troposphere (MOPITT) instrument investigates the 

evolution of tropospheric trace and greenhouse gases 

and their interaction with the climate and biosphere. 

Along with trace gas measurements, studies of greenhouse gases also require information from additional 
Atmospheric Chemistry • Data Products Handbook - Volume 1 145

window with no significant absorption by other gases. 

This wavelength is also close to the peak radiating 

temperature of terrestrial thermal radiation (about 250 

K), making the gas a strong contributor to the socalled “greenhouse effect.” Moreover, CH4

 currently 

appears to be increasing at a rate of one percent per 

year, but the source of this increase is not certain, and 

there is evidence that it began decelerating during the 

1980s. Improved knowledge of the source strengths 

and distributions will help to resolve this uncertainty. 

Knowledge of the global column distribution of CH4

taken with dynamical data may lead to a better quantitative understanding of its biogeochemical cycles. 

The Level 2 CH4 data set may be used to produce 

Level 3 global grids, which, in turn, may be used 

to develop regional and seasonal averages and other 

special products.

 Measurements from the Terra platform are useful 

in observing the distribution and variation of the 

sources of CH4. While ground-based observations 

tend to generate a specified accuracy over time, they 

cannot observe the spatial and temporal variations of 

the sources that are of most interest at present. Studies 

conducted in conjunction with other measurements of 

meteorological conditions (such as local wind speed 

and direction) may determine the source magnitude 

from the atmospheric perturbations to the background 

amount. The combination of space-based and in situ

measurements can be synthesized with other databases of gaseous concentrations and emissions into 

global climate models for a better understanding of 

the overall problem.

 In addition to the MOPITT product, Total Ozone 

Burden (a parameter in MOD07) measures total column ozone to monitor the potential environmental 

hazard of anthropogenic ozone depletion, and to help 

develop and use atmospheric correction models.

Product Interdependency and 

Continuity with Heritage Data

The MOPITT instrument heritage includes the 

Nimbus-7 Stratospheric and Mesospheric Sounder 

(SAMS) instrument and the Improved Stratospheric 

and Mesospheric Sounder (ISAMS) instrument on 

UARS, which employed correlation spectroscopy 

systems similar to that employed by MOPITT. Currently operating stratospheric instruments such as 

TOMS and others onboard UARS are helping scientists learn about the exchange of trace gases between 

the stratosphere and troposphere. Prior to the launch 

of Terra, surface fluxes of some land- and ocean-derived trace gases were estimated using AVHRR and 

SeaWiFS observations and 4-D data assimilation. 

Overview

Suggested Reading

Brasseur, G. P., and R. G. Prinn, 1992.

Brenninkmeijer, C. A. M. et al., 1992.

Chicano, R. J., and R. S. Oremland, 1988.

Crutzen, P. J., 1991.

Dickerson, R. R. et al., 1987.

Dlugokencky, E. J. et al., 1994.

Drummond, J. R., 1992.

Novelli, P. C. et al., 1994.

Reichle, Jr., H. G. et al., 1989.

Steele, L. P. et al., 1992.

Wofsy, S. C. et al., 1972.
146 Data Products Handbook - Volume 1 • Atmospheric Chemistry 

MOPITT CO Profile, CO 

Column, and CH4 Column Data 

(MOP02)

Product Description

The carbon monoxide (CO) vertical profile and CO 

column data, along with methane (CH4) total column, 

make up the three parameters of the MOPITT Level 2 

product. The parameters are retrieved daily over the 

globe with methane column measurements available 

only in the sunlit portion. Horizontal resolution is 

variable from 22 km to ~100 km, depending on cloud 

clearing and pixel averaging, to maintain high signal 

to noise. The profile vertical resolution is about 4 km 

in the troposphere. CO profile and total column precision have been shown to be 10%. Methane column 

precision is expected to be one percent.

Research and Applications

Global CO measurements are very important because 

of the role of the gas in the extremely complicated 

chemical balance of the troposphere. Most of the 

chemistry in this region of the atmosphere is induced 

by the chemical activity of the hydroxyl and similar 

radicals. These agents react rapidly with many trace 

gases of both natural and anthropogenic origin preventing their buildup in the troposphere through the 

creation of chemical products that are rained out or 

lost from the atmosphere by dry deposition. However, 

the concentrations of these radicals are extremely 

difficult to measure due to their short lifetimes and 

low concentrations. The major agents regulating the 

concentrations of the radicals are carbon monoxide, 

ozone, and nitrogen oxide. Global CO measurements 

used in conjunction with atmospheric chemical models therefore provide an important measure of assessing the ability of the atmosphere to “cleanse” itself.

 The Level-2 carbon monoxide parameters also are 

used to produce Level 3 global grids, which in turn 

may be used to develop regional and seasonal averages and other special products. These data products 

provide a time evolution of the distribution of CO in 

the troposphere. From these a better knowledge of the 

chemical interactions, transports, sources, and sinks is 

obtained. Understanding their biogeochemical cycles 

and the intimate interrelation with other gases and 

the climate will lead to better predictions of possible 

effects of anthropogenic activities.

 Measurements from the Terra platform are extremely useful in resolving the temporal and spatial 

Terra / MOPITT

variations in CH4 not observable by ground-based 

networks. Knowledge of the global column distribution of methane taken with dynamical data may 

lead to a better quantitative understanding of its biogeochemical cycles. The Level 2 methane data set 

may be used in the future to produce Level 3 global 

grids, which, in turn, may be used to develop regional 

and seasonal averages and other special products.

 The global distribution of these profiles and column 

amounts are used in descriptive studies of these gases 

on a global basis, providing the first detailed information on their horizontal, vertical, and temporal variations, and their relationships to other activities such 

as biomass burning, industrial activity, thunderstorm 

venting of the boundary layer, etc. There is also a 

wide variety of temporal and spatial scales involved. 

By observing the sources, their magnitudes, and their 

variation we could learn a considerable amount about 

the interaction of the surface and the troposphere. By monitoring the magnitude of the sinks, 

in conjunction with atmospheric chemical models, 

we learn about the chemistry of the troposphere.

Data Set Evolution

The products are derived from measurements of both 

thermal upwelling radiation and the attenuation of 

solar radiation by atmospheric carbon monoxide and 

methane. Absorption due to CO or CH4 is discriminated from other gases by means of length modulator 

cells containing known amounts and pressures of the 

gases. A fast-forward radiance model is used by the 

data processing algorithm to compute average and 

difference signals for known states of the atmosphere. 

The algorithm adjusts the CO and CH4 amounts to 

bring the computed radiances into agreement with 

the observed. Ancillary data from the National Centers for Environmental Prediction (NCEP) are used 

to specify the atmospheric temperature and water 

vapor content in the calculations.

 The particular instrument and algorithm configuration described here has not been previously applied. 

However, the maximum likelihood retrieval scheme 

has been successfully utilized in several space missions remotely measuring atmospheric chemical species. The use of correlation radiometry to determine 

concentrations of CO in the mid troposphere has been 

successfully demonstrated by the Measurement of 

Air Pollution from Satellites (MAPS) instrument, 

which has been flown aboard the Space Shuttle 

several times. The approach of using reflected solar 

radiation to determine the total column amounts of 

an atmospheric gas has been successfully used many 
Atmospheric Chemistry • Terra / MOPITT Data Products Handbook - Volume 1 147

times. Perhaps the most notable application has been 

with NASAʼs Total Ozone Mapping Spectrometer 

(TOMS), first deployed on the Nimbus-7 satellite.

Suggested Reading

Brasseur, G. P., and R. G. Prinn, 1992.

Brenninkmeijer, C. A. M. et al., 1992.

Cicerone, R. J., and R. S. Oremland, 1988.

Clerbaux, C. et al., 2002.

Crutzen, P. J., 1991.

Deeter, M. N. et al., 2002.

Deeter, M. N. et al., 2003.

Dickerson, R. R. et al., 1987.

Dlugokencky, E. J. et al., 1994.

Drummond, J. R., 1992.

Drummond, J. R., and G. S. Mand, 1996.

Edwards, D. P. et al., 1999.

Edwards, D. P. et al., 2003.

Emmons, L. K. et al., 2003.

Granier, C. et al., 1992.

Lamarque, J. F. et al., 2003.

MOPITT Science Team, 1996.

Muller, J. F., and G. P. Brasseur, 1995.

Novelli, P. C. et al., 1994.

Pan, L. et al., 1995.

Pan, L. et al., 1998.

Reichle, Jr., H. G. et al., 1989.

Smith, M. W., and S. R. Shertz, 1996.

Steele, L. P. et al., 1992.

Wang, J. et al., 1999a,b.

Wang, J. et al., 2000.

Warner, J. X. et al., 2001.

Wofsy, S. C. et al., 1972.

Zander, R. P. et al., 1989.

MOPITT CO Profile, CO Column and CH4

Column Data Product Summary

Coverage: Global

Spatial/Temporal Characteristics: CO profiles 

retrieved daily, day and night; column amounts 

retrieved daily in sunlit portion of orbits; 

resolution 22 km with some degradation depending on cloud clearing and pixel averaging

Key Geophysical Parameters: Atmospheric 

total column carbon monoxide; tropospheric 

vertical profile of carbon monoxide; atmospheric total column methane

Key Science Applications: Biogeochemical 

modeling, tropospheric chemical modeling, 

and greenhouse gas modeling

Processing Level: 2

Product Type: Standard, at-launch

Maximum File Size: 35 MB

File Frequency: 1/day

Primary Data Format: HDF-EOS

Browse Available: Yes

Additional Product Information: 

www.eos.ucar.edu/mopitt/index.html

www.atmosp.physics.utoronto.ca/MOPITT/

home.html

DAAC: Langley Atmospheric Sciences Data 

Center, eosweb.larc.nasa.gov

Science Team Contact: J. R. Drummond
149

Chapter 9

Surface Temperatures

of Land and Oceans,

Fire Occurrence, and 

Volcanic Effects

Terra

ASTER

MODIS
150 Data Products Handbook - Volume 1 • Surface Temperatures of Land and Oceans, Fire Occurrence, and Volcanic Effects 

Surface Temperatures of Land and Oceans, Fire 

Occurrence, and Volcanic Effects – An Overview

the land and atmosphere systems that are monitored 

by the fire product.

 Monitoring volcanic activity is important because 

of the disastrous geophysical consequences volcanic 

eruptions have on the Earthʼs atmosphere, including 

increased precipitation, ozone destruction, and the 

lowering of global temperatures. Moreover, cooling of the middle and upper stratosphere—from the 

increase in reflectivity caused by aerosols and the 

reduced absorption of ultraviolet light (because of 

reduced ozone from volcanoes and other sources)—

plays a significant role in climate change.

What Must be Measured?

Surface temperature algorithms require calibrated radiances as a basic input. Since the radiances observed 

have passed through the atmosphere, corrections must 

be made for atmospheric attenuation and scattering, 

hence meteorological data on temperature and water 

vapor profiles, as well as chemical constituents, are 

needed. Also, the radiance emitted by the surface is 

determined by surface emissivity as well as temperature. The land surface pixel can contain a mixture 

of materials having different emissivity, geometric, 

and topographic characteristics that must be known 

before temperature can be computed. Land material emissivity also can have significant variability 

as a function of wavelength, and for the mid-wave 

IR bands significant reflectance can exist such that 

solar reflected energy must be predicted and removed 

from the observed radiance. The emissivity of the sea 

surface is assumed to be unity and the sea-surface 

temperature algorithm in this case produces what 

is called the brightness temperature. The ancillary 

information needed is supplied by external sources 

and precomputed databases keyed on surface material provided by the land-cover-type product and the 

global ocean masks.

 Fire detection and measurement must be implemented with sensor bands that do not saturate at fire 

temperatures. This requirement is met with MODIS 

by a special fire channel that saturates at 500 K, which 

provides sufficient range to segment fire intensity. 

ASTER temperature data are used in combination 

with the MODIS global fire data for high-resolution 

mapping of fires and in the monitoring and analysis 

of volcanic processes.

 Overview

Relationship to Global Change Issues

The surface temperature of the land and ocean is 

a parameter that determines the vigor of biogeochemical activity and the rate of heat flow and water 

transport between the surface and the atmosphere. 

These in turn influence global environmental processes. On land, soil and canopy temperature are 

among the main determinants of the rate of growth 

of vegetation, and they govern seasonal starting and 

termination of growth. Hydrologic processes such 

as evapotranspiration and snow and ice melt are 

highly sensitive to surface temperature fluctuation. 

In addition, surface temperature has been shown to 

be an important discriminating factor in classification of land surface types, which supports the global 

land cover type product. For the oceans, the surface 

temperature influences the rate of growth of phytoplankton, the rate of absorption of carbon dioxide 

and other gases by sea water, and the rate of flow of 

heat to and from the atmosphere. Thus, temperature 

products are key inputs to many of the EOS product 

algorithms as well as providing data for global temperature mapping and change observation.

 In the ocean, phytoplankton chemically fix carbon 

through photosynthesis, which takes in dissolved carbon dioxide in sea water and produces oxygen that 

enables phytoplankton to grow. This process captures 

about an equal amount of carbon as does photosynthesis by land vegetation. Changes in the amount of 

phytoplankton indicate the change in productivity of 

the oceans and provide a key ocean link for global 

climate change monitoring. Ocean and land biological 

productivity, heat exchange, and water exchange processes are tightly coupled; global change models take 

all interactions into account in global climate change 

prediction. In addition, these annual productivity 

products can be used for global- and regional- scale 

studies of ocean productivity, for comparisons with 

annual summations of daily analytic algorithms, and 

for comparison of global biogeochemical models to 

continually improve the technology for global change 

analysis.

 The need also exists for information on the occurrence of fires on the land surface, since fire 

changes the surface cover type and releases gases 

and particulate matter into the atmosphere, affecting 

ecosystems and atmospheric chemistry on a rapid and 

intense scale. The burning of oil slicks on water is 

also a significant, albeit infrequent, intense input into 
Surface Temperatures of Land and Oceans, Fire Occurrence, and Volcanic Effects • Data Products Handbook - Volume 1 151

Product Overview

The EOS land and ocean surface temperature and fire 

products are provided by MODIS and ASTER instruments. The MODIS land surface temperature product 

(MOD11) provides the needed composite temperature 

of each pixel and the estimated emissivity from database information plus adjustments made possible 

by the multiwindow algorithm. The accuracy is better than 1 K for materials with known emissivities. 

The ASTER surface temperature product (AST05, 

AST08) has high spatial resolution and will be used 

for comparison and validation of the MODIS temperature products and for the monitoring and analysis 

of volcanic processes. The sea surface temperature 

(MOD28) derived from radiance measurements is an 

estimate of the skin temperature (top millimeter) of 

the 1-km pixel viewed by MODIS. The algorithm is 

similar to the land surface temperature algorithm in 

that it uses multiple atmospheric window techniques 

to estimate the atmospheric parameters that are required to compensate for absorption and scattering. 

The bulk temperature of the near-surface ocean is 

the temperature of the upper 10-20 cm to 1 meter as 

measured by conventional thermometers on buoys 

and ships. Extensive analysis has been done of satellite and in situ data to enable the algorithm to estimate 

bulk temperature as well as skin temperature.

 The thermal anomaly product (MOD14, MOD40) 

contains information unique to understanding the 

characteristics of fires, including energy emitted, 

brightness temperature, and the ratio of smoldering 

to flaming area and is available for both day and 

night periods. The composites produced include a 

monthly, day and night fire occurrence aggregation 

and a summary of the number of fires in classes 

related to the strength of the fire. The ASTER land 

surface temperature product will provide high resolution mapping of fires as a complement to MODIS 

global fire data.

Product Interdependency and 

Continuity with Heritage Data

The land surface temperature product requires MODIS calibrated radiances and the cloud mask product 

as inputs. Also, atmospheric temperature and water 

vapor profiles are needed along with a database of 

emissivities, the use of which is driven by the land 

cover classification products that enable surface 

material to be specified for each pixel. The land 

surface temperature product from ASTER is needed 

for comparison and spatial variation analysis of the 

MODIS land surface temperature product over test 

sites. The land surface temperature product is an input 

to other land products including vegetation indices, 

evapotranspiration, and net primary production and 

as a background temperature estimate for the fire 

product.

 Similarly, the sea surface temperature product 

uses MODIS calibrated radiances and the cloud 

mask product, but does not require the complex 

emissivity estimation databases and algorithms that 

the land surface temperature product needs. Sun glint 

is a significant source of error in the mid-wave IR 

bands, so the mask product is critical to identifying 

glint pixels as is sea surface wind ancillary data that 

are used to estimate the magnitude of the glint area 

radiance.

 Existing meteorological stations on land and 

ship and buoy-based instruments at sea measure 

surface temperatures at large numbers of worldwide 

locations; however, these measurements are not 

adequate in terms of the radiative phenomenon or 

spatial coverage. Land- and sea-surface temperature 

data taken by TRMM and Terra add to the valuable 

input already provided by AVHRR, HIRS, Landsat, 

CZCS, and SeaWiFS, providing a large, long-term 

global land and sea surface temperature data set in 

order that we may more fully understand the effects 

of the various Earth-system cycles on temperature.

Suggested Reading

Asrar, G., and J. Dozier, 1994.

Becker, F., 1987.

Chahine, M. T., 1980.

Justice, C. O., and S. A. Korontzi, 2001.

Kaufman, Y. J. et al., 1991.

Price, J. C., 1983.

Robinson, J. M., 1991.

Running, S. W. et al., 1994.

Overview
152 Data Products Handbook - Volume 1 • Surface Temperatures of Land and Oceans, Fire Occurrence, and Volcanic Effects 

ASTER Surface Emissivity 

(AST05) and Surface Kinetic 

Temperature (AST08) 

Product Description

This Level 2 product (AST05) contains surface 

emissivity at 90-m resolution generated only over 

the land from ASTERʼs five thermal-IR channels. 

Surface emissivity is required to derive land-surface 

temperature (AST08) data, also at a resolution of 90 

m. Land surface temperatures are determined from 

Planckʼs Law, using the emissivities to scale the 

measured radiances after correction for atmospheric 

effects. Pixels classified as “cloud” will have no atmospheric correction due to a lack of knowledge of 

cloud height, and the cloud temperature will be given 

as the brightness temperature at sensor. 

Research and Applications

The emissivity product is critical for deriving land 

surface temperatures. It is therefore important in studies of surface energy and water balance. The emissivity product is also useful for mapping geologic 

and land-cover features. The derived land surface 

temperature has applications in studies of surface 

energy and water balance. Temperature data will 

be used in the monitoring and analysis of volcanic 

processes; day and night temperature data will be 

used to estimate thermal inertia; and thermal data 

will be used for high-resolution mapping of fires as 

a complement to MODIS global fire data.

Data Set Evolution

Current sensors provided only limited information 

useful for deriving surface emissivity, and researchers 

currently are required to use emissivity surrogates 

such as land-cover type or vegetation index in making 

rough estimates of emissivity and hence land surface 

temperatures. The five thermal-IR channels of the 

ASTER instrument enable direct surface emissivity 

estimates. Mapping of thermal features from optical 

sensors such as Landsat and AVHRR has been used 

for many developmental studies. These instruments, 

however, lack the spectral coverage, resolution, and 

radiometric accuracy that will be provided by the 

ASTER instrument.

Suggested Reading

Dozier, J., and Z. Wan, 1994.

Hook, S. J. et al., 1992.

Gillespie, A. et al., 1998.

Ogawa, O. et al., 2003.

Kahle, A. B., 1986.

ASTER Surface Emissivity and Surface 

Kinetic Temperature Summary

Coverage: Regional, land surface

Spatial/Temporal Characteristics: 90 m

Key Science Applications: Local heat balance, 

volcanology, biomass burning, thermal inertia

Key Geophysical Parameters: Surface emissivity, surface temperature

Processing Level: 2

Product Type: Standard, on request

Maximum File Size: 9 MB (Surface Emissivity), 4 MB (Surface Kinetic Energy)

File Frequency: On demand

Primary Data Format: HDF-EOS

Additional Product Information:

edcdaac.usgs.gov/aster/asterdataprod.html

DAAC: EDC Land Processes DAAC, 

edcdaac.usgs.gov

Science Team Contact: A. Gillespie

Terra / ASTER
Surface Temperatures of Land and Oceans, Fire Occurrence, and Volcanic Effects • Data Products Handbook - Volume 1 153

These ASTER images of Death Valley were taken on October 1, 2000. The top image 

represents Band 10 Emissivity and the lower image shows surface temperature. On the 

internet, visit visibleearth.nasa.gov to view a variety of ASTER data images.

Terra / ASTER
154 Data Products Handbook - Volume 1 • Surface Temperatures of Land and Oceans, Fire Occurrence, and Volcanic Effects 

MODIS Land Surface 

Temperature (LST) and 

Emissivity (MOD11)

Product Description

The Terra MOD11 product includes Level 2 and 

3 (day and night) LST and emissivity products 

retrieved from Terra MODIS data over global land 

surface under clear-sky conditions. The generalized 

split window LST algorithm is used to retrieve LSTs 

in the Level-2 product (MOD11_L2) for MODIS pixels with emissivities in bands 31 and 32 estimated 

from land cover types in the MODIS land-cover and 

snow products through look-up tables established by 

thermal infrared (TIR) BRDF and emissivity modeling. The LST and emissivity values of all pixels 

in MOD11_L2 files are mapped onto 1-km grids 

in the sinusoidal projection to generate the level-3 

1-km LST product (MOD11A1). The physics-based 

day/night LST algorithm is used to simultaneously 

retrieve surface band emissivities and temperatures 

in the level-3 5-km MOD11B1 product from a pair 

of daytime and nighttime MODIS 

observations in bands 20, 22, 23, 

29, and 31-33. The LST product includes local solar time and viewing 

angle of the MODIS observation 

used in LST retrieval, and quality 

flags for each data set of LST and 

emissivity. The daily LST products 

are used to generate global gridded 

8-day and monthly LST products 

(MOD11C2 and MOD11C3) for 

day and night conditions.

Research and Applications

Land surface temperature is a good 

indicator of both the energy balance at the Earthʼs surface and the 

greenhouse effect because it is one 

of the key parameters in the physics of the land-surface processes. 

It is required for a wide variety of 

climate, hydrological, ecological, 

and biogeochemical studies. This 

product is used in generating other 

MODIS products and in a variety of 

EOS interdisciplinary studies.

Terra / MODIS

Data Set Evolution

The MODIS LST products build and improve upon 

the experience of LST retrieval from AVHRR and 

MODIS Airborne Simulator (MAS) data. The MODIS instrument has been designed to include TIR 

bands suitable for LST and emissivity retrieval at the 

global scale. The inputs used in the LST algorithms 

include calibrated radiance, geolocation, cloudmask, 

atmospheric temperature and water profile, land-cover, snow, and BRDF data in the MODIS products. 

The combination of Terra and Aqua provides four 

observations per day, giving a good sampling of the 

diurnal cycle of the LST. The accuracy of the LST 

products has been validated within 1K with in situ

radiometer measurements at nine validation sites 

including lakes, playa, grasslands, snow, rice, and 

soybean fields in wide ranges of the LST and atmospheric conditions.

220 280 340

220 280 340

The global monthly daytime LSTs (K) in January (top) and July (bottom) 2001 at equal 

latitude/longitude grids with a size of 1/4 degree were generated from the daily LST 

product (MOD11B1). Areas in black are grids in ocean and cloudy conditions. On the 

internet, visit visibleearth.nasa.gov to view a variety of MODIS data images.
Surface Temperatures of Land and Oceans, Fire Occurrence, and Volcanic Effects • Terra / MODIS Data Products Handbook - Volume 1 155

Suggested Reading

Justice, C. et al., 1998. 

Running, S. W. et al., 1994.

Salisbury, J. W., and D. M. DʼAria, 1992.

Snyder, W., and Z. Wan, 1996.

Snyder, W., and Z. Wan, 1998.

Wan, Z., and J. Dozier, 1996.

Wan, Z., and Z.-L. Li, 1997.

Wan, Z. et al., 2002.

MODIS Land Surface Temperature and 

Emissivity Summary

Coverage: Global land surface

Spatial/Temporal Characteristics:

• 1 km/day (MOD11_L2)

• 0.93 km/day (MOD11A1)

• 0.93 km/8-day (MOD11A2)

• 4.64 km/day (MOD11B1)

• 1/20 degree/8-day (MOD11C2)

• 1/20 degree daily and monthly (MOD11C1 

 and MOD11C3)

Key Science Applications: Inputs to climate,

hydrological, ecological modeling

Key Geophysical Parameters: Land-surface 

temperature, land-surface emissivity

Processing Level: 2 for MOD11_L2; 3 for 

MOD11A1, MOD11A2, MOD11B1, MOD11C

Product Type: Standard, at-launch and postlaunch

Maximum File Size: 26 MB (MOD11_L2), 

24 MB (MOD11A1 and MOD11A2), 2 MB 

(MOD11B1), 441 MB (MOD11C1), 

467 MB (MOD11C2), 622 MB (MOD11C3)

File Frequency: 288/day (MOD11_L2), 

317/day (MOD11A1 and MOD11B1), 317/8-

day (MOD11A2), 1/day (MOD11C1), 1/8-day 

(MOD11C2), and 1/month (MOD11C3)

Primary Data Format: HDF-EOS

Additional Product Information:

modis-land.gsfc.nasa.gov/products/

products.asp?ProdFamID=8

DAAC: EDC Land Processes DAAC, 

edcdaac.usgs.gov

Science Team Contact: Z. Wan
156 Data Products Handbook - Volume 1 • Surface Temperatures of Land and Oceans, Fire Occurrence, and Volcanic Effects 

MODIS Thermal Anomalies 

– Fires (MOD14); MODIS Burn 

Scars (MOD40)

Product Description

The MODIS Thermal Anomalies product includes 

fire occurrence (day/night), fire location, the logical criteria used for the fire selection, and an energy 

calculation for each fire. The product also includes 

composite 8-day-and-night fire occurrence (full resolution), composite daily day-and-night fire occurrence 

(full resolution), and a gridded 0.25° summary of fire 

counts per class (daily/monthly). The Level 2 product 

includes various fire-related parameters including the 

occurrence of day and nighttime thermal anomalies, 

flagged and grouped into different temperature classes 

with emitted energy from the fire. These parameters 

are retrieved daily at 1-km resolution. The fire product 

uses the special fire channel at 3.9 μm that saturates 

at 500 K and the high-saturation level of the 11-μm 

channel. The standard products will include the area 

burned.

Research and Applications

Fire is an important process within a significant number of terrestrial biomes, and the release of gases 

and particulate matter during biomass burning is an 

important contributor to the chemical reactions and 

physical processes taking place in the atmosphere. 

Fire is a significant and continuous factor in the 

ecology of savannas, boreal forests, and tundra, and 

plays a central role in deforestation. Fire information will be used to drive regional emissions models, 

trace-gas transport models, and mesoscale models 

of atmospheric chemistry. Important impacts of fires 

include:

• changes of physical state of vegetation and release 

of greenhouse gases;

• release of chemically reactive gases during 

biomass burning;

• release of soot and other particulate matter during 

fires;

• changes in the exchange of energy and water between land surfaces and the atmosphere; and

• changes in plant community development and soil 

nutrient, temperature, and moisture, and cloud development and reflectivity.

Data Set Evolution

The MODIS fire products build and improve upon 

the experience of fire assessment primarily using the 

NOAA AVHRR and GOES systems. Currently, no 

one sensing system provides the instrument characteristics needed for an effective global fire-monitoring 

program. The MODIS sensor has been designed to include characteristics specifically for fire detection and 

provides a unique capability over existing sensors in 

terms of fire monitoring. The locational accuracy and 

improved instrument characterization and calibration 

will enable unprecedented fire-monitoring data sets. 

The combination of Terra and Aqua data provides four 

observations per day, giving a good sampling of the 

diurnal cycle of fire activity. MODIS will also offer 

unique spatial and radiometric capabilities for burnscar detection. Automatic procedures for burn-scar 

detection are being developed to provide a standard 

burn-scar product.

Trail-blazing firefighters and land managers now use NASA satellite data to combat wildfires. This image of the southeastern 

coast of Australia on January 2, 2002, from MODIS onboard the 

Terra satellite, shows thick plumes of grayish smoke streaming 

eastward from more than a dozen active fires. Fires encircle the 

city of Sydney, which stands out in tan against the surrounding 

green vegetation. In a collaboration between NASA, the University of Maryland, and the U.S. Department of Agricultureʼs 

Forest Service, firefighters receive maps and satellite images of 

current active fires, and use them for strategic planning. After 

the fire is controlled, land managers use the satellite images and 

maps for post-fire rehabilitation and protection of water quality. 

Image credit: Jacques Descloitres, MODIS Land Rapid Response 

Team at NASA/GSFC.

Terra / MODIS
Surface Temperatures of Land and Oceans, Fire Occurrence, and Volcanic Effects • Data Products Handbook - Volume 1 157

MODIS Sea Surface 

Temperature (SST) (MOD28)

Product Description

This Level 2 and 3 product provides sea surface temperature (SST) at 1-km (Level 2) and 4.6 km, 36 km, 

and 1° (Level 3) resolutions over the global oceans. 

This product consists of four global SST fields: daytime (D1) and nighttime (N1) SST derived from the 

11-micron channel, and daytime (D2) and nighttime 

(N2) SST derived from the 4-micron channel. In addition, a quality-assessment parameter is included for 

each pixel. The Level 2 product is produced daily 

and used to generate the gridded Level 3 products 

daily, 8-day weekly, monthly, and yearly for day and 

night conditions. A quality parameter is provided for 

each data set.

Research and Applications

The global distribution and variability of sea surface temperature are key inputs to Earth energy and 

hydrological balance studies and long-term climate 

change studies. In addition, sea surface temperature is 

required by a number of MODIS algorithms including 

those for precipitable water, productivity, oceanic 

aerosol properties, and temperature and water-vapor 

profiles. MODIS sea surface temperature retrievals 

will be incorporated into a match-up database with 

radiance and buoy sea surface temperature observations (see MOD32). The daytime SST derived from 

the 4 micron channel (D2) is affected by reflected 

solar radiation, and therefore the D1 (11 micron) 

SST is preferable for daytime applications. During 

the night, the surface ocean is more homogeneously 

mixed, and thus the skin temperature is more representative of the bulk upper-layer temperature. The 

N2 has a higher signal-to-noise than N1, but both 

fields are validated.

Data Set Evolution

Sea surface temperature determination is based on 

MODIS-calibrated mid-and far-IR radiances (Bands 

20, 22, 23, 31, and 32 from MOD02), using an algorithm that exploits the differences in atmospheric 

transmissivity in the different IR bands to enable 

highly accurate estimation of the atmospheric effects. 

A land mask is used to mark nonwater pixels while an 

ice-extent mask limits polar sea coverage. A sequence 

of spatial and temporal homogeneity tests is applied 

Terra / MODIS

Suggested Reading

Ahern, F. et al., 2001.

Andreae, M. O. et al., 1994.

Giglio L. et al., 1999.

Justice C. O. et al., 1993.

Justice C. O. et al., 1998.

Justice C. O. et al., 2002.

Kaufman, Y. J. et al., 1990.

Kaufman, Y. J. et al., 1998.

Levine, J. S., 1991.

Penner, J. E. et al., 1992.

Roy, D. P. et al., 2002.

Roy, D. P. et al., 1999.

MODIS Thermal Anomalies – Fires and 

Burn Scars Summary

Coverage: Global, daytime/nighttime

Spatial/Temporal Characteristics: 1 km and 

0.25°/1-day (MOD40 only), 8-day, monthly

Key Geophysical Parameters: Fire occurrence 

and class, fire selection criteria, fire location, 

smoldering and flaming ratio, burned area

Processing Level: 2, 3 for MOD14; 4 for 

MOD40

Product Type: Standard, at-launch and postlaunch

Maximum File Size: 81 MB (MOD14), 40 MB

(MOD40)

File Frequency: 288/day (Level 2), 294/day, 

8-day or 16-day (Level 3 and 4)

Primary Data Format: HDF

Additional Product Information: 

modis-land.gsfc.nasa.gov/products/

products.asp?ProdFamID=1

DAAC: EDC Land Processes DAAC,

edcdaac.usgs.gov

Science Team Contacts: Y. J. Kaufman, 

C. O. Justice
158 Data Products Handbook - Volume 1 • Surface Temperatures of Land and Oceans, Fire Occurrence, and Volcanic Effects

This is a grayscale version of a false-color image that shows a one-month composite for May 2001 developed from MODIS 

data. The MODIS sea surface temperature product is particularly useful in studies of temperature anomalies, such as 

El Niño, as well as research into how air-sea interactions drive changes in weather and climate patterns. Image courtesy 

of the MODIS Ocean Group, NASA GSFC, and the University of Miami. On the internet, visit visibleearth.nasa.gov 

to view a variety of MODIS data images.

Terra / MODIS

to validate the quality of the cloud-free observations. 

The AIRS SST estimate is used as a near-real-time 

quality assessment of skin temperature. Visible and 

near-IR radiances (Bands 3, 4, 5, 6) are used as a secondary cloud flag when the cloud-screening product 

is not available. 

Suggested Reading

Abbott, M. R., and D. B. Chelton, 1991.

Esaias, W. E. et al., 1998.

MODIS Sea Surface Temperature Summary

Coverage: Global ocean surface, clear-sky 

only

Spatial/Temporal Characteristics: 1 km/daily 

(Level 2); 4.6 km, 36 km, 1°/daily, 8-day, 

monthly, yearly (Level 3)

Key Science Applications: Energy and hydrological balance, climate-change models

Key Geophysical Parameters: Sea surface 

temperature

Processing Level: 2, 3

Product Type: Standard, at-launch

Maximum File Size: 33 MB (Level 2); 640 MB 

binned, 134 MB mapped (Level 3)

File Frequency: 288/day (Daily Level 2), 4/day 

(Daily Level 3), 4/8-day (8-day Level 3),

4/month (Monthly Level 3), 4/year (Yearly 

Level 3)

Primary Data Format: HDF-EOS

Browse Available: 36 km sample imagery 

available at the GES DAAC (Level 3 only)

Additional Product Information: 

modis-ocean.gsfc.nasa.gov/dataprod.html

DAAC: Goddard Space Flight Center Earth 

Sciences DAAC, daac.gsfc.nasa.gov

Science Team Contacts: O. Brown, 

P. J. Minnett
159

Chapter 10

Vegetation Dynamics,

Land Cover, and 

Land Cover Dynamics

Terra

MODIS
160 Data Products Handbook - Volume 1 • Vegetation Dynamics, Land Cover, and Land Cover Dynamics 

Vegetation Dynamics, Land Cover, and Land Cover 

Dynamics – An Overview

are radiometric measures of the amount of vegetation present on the ground in a particular pixel. The 

indices are obtained from measurements of reflected 

visible and near-infrared energy from vegetation and 

are called normalized difference vegetation indices 

(NDVI). These indices have values between -1 and 1 

and increase with increasing amounts of vegetation. 

Several new indices have been developed that compensate for soil and atmospheric effects, and these 

are combined into the enhanced vegetation index 

(EVI), also produced by MODIS. The indices are 

produced at different resolutions and time-averaging 

intervals to provide measures of vegetation density 

and productivity, which are required by global change 

models.

 The Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) provide 

information on green leaf area of Earthʼs vegetation 

and how much sunlight the leaves are absorbing. LAI 

defines an important structural property of a plant 

canopy, which is the one-sided leaf area per unit 

ground. FPAR measures the proportion of available 

radiation in the photosynthetically active wavelengths 

(0.4 to 0.7 mm) that a canopy absorbs and varies 

between 0 and 1. These parameters derived from the 

Surface Reflectance and the Land Cover Type products. A three-dimensional canopy radiation model is 

used to simulate spectral reflectances of different 

types and amounts of vegetation. The computer 

then compares the images collected by MODIS to 

the simulated images until it finds just the right match. 

Next it calculates the corresponding leaf area and 

absorbed radiation. These two products are essential 

in calculating terrestrial energy, carbon, water cycle 

processes, and biogeochemistry of vegetation.

Product Description – Land Cover and 

Land Cover Dynamics

Land Cover Type (MOD12) maps global terrestrial 

land cover at a 1-km resolution, using five different 

sets of cover type labels targeted to different applications. The cover type labels indicate broad classes 

of vegetation distinguished by life-form, leaf type, 

periodicity, and cover proportion. The land cover 

dynamics product detects areas of significant interannual change, such as flooding, drought, or burning. In addition, it provides the dates of transition 

for four phenological stages of vegetation for each 

 Overview

Relationship to Global Change Issues

About 30% of the Earthʼs surface is covered by land, 

and much of this is vegetated, making land surface 

processes important components of the terrestrial 

climate system. Vegetation, land cover, and landuse change provide the basis for understanding land 

and water resource management as well as a range 

of research and global monitoring objectives, including global carbon modeling, greenhouse gas emission 

inventories, regional environment and development 

assessments, and forest resource management. Ecologists and climatologists have developed models that 

describe the matter and energy exchanges, but lack of 

long-term global data limits their use in Earth-system 

models. Additionally, we lack good global data on 

surface albedoes over land, on the distribution of soils 

and important properties of vegetation morphology 

and physiology, and on water storage in soils and 

snow cover and their relationship to runoff. Incorporation of this information in the land-surface component of climate models will improve estimates of 

exchanges of water and energy with the atmosphere 

and lead to more realistic climate simulations

Product Description – Vegetation

The land vegetation products provide information 

on terrestrial systems which have important links to 

climate and atmospheric composition that are needed 

for development and application of models of the 

Earthʼs system. These links involve exchanges of energy and moisture, radiatively active trace gases (e.g., 

carbon dioxide and methane) and photochemically 

active species such as methane, nitric oxide, and nonmethane hydrocarbons that soils, plants, and biomass 

burning release.

 The Terra vegetation products provide the basic 

measurements of the photosynthetic engines that 

drive carbon transport on land. For land areas, 

vegetation density and vegetation canopy factors 

are produced which are used to generate the land 

primary productivity, the amount of carbon fixed by 

vegetation growth per day or longer unit of times such 

as per year. The MODIS vegetation measurements 

are provided by the Vegetation Index (MOD13), Leaf 

Area Index and Fraction of Photosynthetically Active 

Radiation (FPAR) (MOD15), and Vegetation Cover 

Conversion (MOD44) products. Vegetation indices 
Vegetation Dynamics, Land Cover, and Land Cover Dynamics • Data Products Handbook - Volume 1 161

pixel during the prior year. Land cover, as well as 

both human and natural alteration of land cover, plays 

a major role in global-scale patterns of climate and 

biogeochemistry.

 The primary layer of the land cover product recognizes 17 basic classes of land cover types following 

the International Geosphere-Biosphere Program 

(IGBP) scheme. Additional layers of labels include 

the University of Maryland land cover classes; a 

set of 6 structural classes used as input to the LAI/

FPAR product algorithm; 6 broad biome types for the 

photosynthesis/net primary productivity algorithm; 

and a future set of plant functional type labels for 

use in the NCAR community land model.

 The land cover dynamics product will detect and 

categorize changes in terrestrial features and processes on a global scale. The land-cover dynamics 

algorithm is independent of the land cover classification algorithm and compares, pixel-by-pixel, the 

temporal development curve of a set of biophysical 

and spatial indicators derived from MODIS data to 

that of the prior year. This product also contains information on the phenology of each pixel, quantifying 

the dates of onset of greenness, maturity, scenescence, 

and dormancy as experienced in the prior year. It will 

be available late in 2002 or early in 2003.

Product Description – MISR Land 

Surface Data

The MISR instrument provides a variety of information on the Earthʼs surface. MISRʼs Aerosol and 

Surface Products (MIS05, MIS08, MIS09) are included in this chapter.

Product Interdependency and 

Continuity with Heritage Data

The land products flow in a chain from the Level 

2 reflectance and land cover type parameters to the 

biophysical- and biochemical-derived products that 

are inputs to the Earth system models. 

 Specific product dependencies for the land vegetation indices are MODIS radiance and imagery 

products, including Level 1B calibrated radiance, 

surface leaving radiance and aerosol optical depth. 

The Surface Reflectances and Land Cover type are 

key inputs to Leaf Area Index and FPAR.

 Each instrument product set has its own science 

requirements and objectives and support the generation and validation of other instrument products. 

Vegetation Index products have been produced for 

many years from AVHRR and other instrument data 

Overview

and MODIS produces a “continuity” NDVI to maintain the NDVI time series using an algorithm that 

closely approximates the heritage index. A worldwide 

database has been created with the data and EOS will 

continue this series.

References

Asrar, G., and J. Dozier, 1994.

Knyazikhin, Y. et al., 1998.

Li, X. et al., 1995.

Moody, A., and C. E. Woodcock, 1995.

Myneni, R. et al., 2002.

Panferov, O. et al., 2001.

Tian, Y. et al., 2002.

Wang, Y. et al., 2001.
162 Data Products Handbook - Volume 1 Terra / MODIS • Vegetation Dynamics, Land Cover, and Land Cover Dynamics 

Justice C. O. et al., 2002.

Kaufman Y. J. et al., 1997.

Petitcollin F., and E. F. Vermote, 2002.

Roger J. C., and E. F. Vermote, 1997.

Roger J. C., and E. F. Vermote, 1998.

Vermote E. F. et al., 1997.

Vermote E. F. et al., 1997.

Vermote E. F. et al., 1997.

Vermote E. F. et al., 2001.

Vermote E. F. et al., 2002a,b.

Vermote, E. F., and D. P. Roy, 2002.

Wang Y. J. et al., 2002.

Wang Y. J. et al., 2001.

Wolfe R. E. et al., 1998.

Wolfe R. E. et al., 2002.

MODIS Surface Reflectance; Atmospheric 

Correction Algorithm Products Summary

Coverage: Global land surface

Spatial/Temporal Characteristics: Bands 1 and 

2, 250 m; bands 3-7, 500 m; daily and 8-day

Key Science Applications: Global climate 

modeling, regional climate modeling, surfaceenergy-balance modeling, land cover 

characterization

Key Geophysical Parameters: Surface reflectance

Processing Level: 2, 2G, 3

Product Type: Standard, at-launch

Maximum File Size: 424 MB

File Frequency: 144/day (Level 2) and 294/day 

or 8-day (Level 2G and 3)

Primary Data Format: HDF-EOS

Additional Product Information: 

modis-land.gsfc.nasa.gov/mod09/

DAAC: EDC Land Processes DAAC,

edcdaac.usgs.gov

Science Team Contact: E. Vermote

MODIS Surface Reflectance; 

Atmospheric Correction 

Algorithm Products (also 

called Spectral Reflectance) 

(MOD09)

Product Description

The MODIS Surface-Reflectance Product (MOD09) 

is computed from the MODIS Level 1B land bands 

1, 2, 3, 4, 5, 6, and 7 (centered at 648 nm, 858 nm, 

470 nm, 555 nm, 1240 nm, 1640 nm, and 2130 nm, 

respectively). The product is an estimate of the surface spectral reflectance for each band as it would 

have been measured at ground level if there were no 

atmospheric scattering or absorption. 

 The correction scheme includes corrections for 

the effect of atmospheric gases, aerosols, and thin 

cirrus clouds; it is applied to all noncloudy MOD35 

Level 1B pixels that pass the Level 1B quality control. 

The correction uses band 26 to detect cirrus cloud, 

water vapor from MOD05, aerosol from MOD04, 

and ozone from MOD07; best-available climatology 

is used if the MODIS water vapor, aerosol, or ozone 

products are unavailable. Also, the correction uses 

MOD43, BRDF without topography, from the previous 16-day time period for the atmosphere-BRDF 

coupling term.

Research and Applications

The surface-reflectance product is the input for product generation for several land products: Vegetation 

Indices (VIs), BRDF, thermal anomaly, snow/ice, and 

Fraction of Photosynthetically Active Radiation/Leaf 

Area Index (FPAR/LAI). It is, therefore, an important 

and essential product. The at-launch version is fully 

operational.

Data Set Evolution

The Aqua MODIS product is based upon the latest 

version of the Terra MODIS product.

Suggested Reading

Holben B. N. et al., 1998.

Justice C. O. et al., 2002.

Justice, C. O. et al., 2001.
Vegetation Dynamics, Land Cover, and Land Cover Dynamics • Terra / MODIS Data Products Handbook - Volume 1 163

Large Aerosol Effects

Level 1B granule, band 3 (470nm) gray image over South Africa., the white rectangle 

delineates the area enlarged in Figures 2.

1a

1b

Surface reflectance granule, band 3 

(470nm) gray image over South Africa. 

Land features are clearly enhanced when 

compared to level 1B. In addition to molecular scattering correction which is important in that band, large aerosol effects 

have been corrected in the left side of the 

image where surface features were barely 

visible in L1B data.

2a 2b

Figure 2a: L1B, band 3 (470nm), over the subarea deliminited by the black rectangle in Figure 1a (above). Smoke plumes are clearly 

visible. Figure 2b: Surface reflectance, band 3 (470 nm), over the subarea delimited by the white rectangle in Figure 1a. Smoke plumes 

(aerosols) have been corrected, the fence line is now visible in the corrected data.

Fenceline

Smoke plumes
164 Data Products Handbook - Volume 1 • Vegetation Dynamics, Land Cover, and Land Cover Dynamics 

times but combines analyses of change in multispectral-multitemporal data vectors with models of 

vegetation-change mechanisms to recognize both 

the type of change and its intensity. It will highlight 

interannual variability in particular, distinguishing, 

for example, regions of flooding, drought, and burning as compared to the prior year. This product also 

provides phenologic information for each 1-km pixel 

that quantifies the date of onset of greenup, maturity, 

senescence, and dormancy each pixel during the prior 

year. The land cover dynamics product is prepared 

independently from the 250-m Land Cover Change 

Product and uses a different algorithm.

 In addition to the basic 1-km product, summary 

products containing proportions of land covers and 

change characteristics are available at one-quarter, 

one-half, and 1-degree resolutions.

Research and Applications

This product is used for biophysical and biogeochemical parameterization of land cover for input 

to global- and regional-scale models of climate, 

hydrologic processes, and biogeochemical cycling. 

Examples of such biogeophysical parameters include 

biomass, cover fraction, and photosynthetic efficiency 

for carbon balance models, as well as albedo, roughMODIS Land Cover Type

(MOD12)

Product Description

This Level 3 product contains land cover type and 

land cover dynamics parameters, which are produced 

at 1-km resolution on a quarterly basis. There are two 

file types provided. The primary land cover product 

(MOD12Q1) identifies 17 categories of land cover 

plus water following the IGBP global vegetation 

database, which defines nine classes of natural vegetation, three classes of developed lands, two classes 

of mosaic lands, and three classes of nonvegetated 

lands (snow/ice, bare soil/rocks, water). In addition, 

the product provides land cover type labels following the University of Maryland scheme; global 

structural cover types required for input to the LAI/

FPAR algorithm; global biome types for input to the 

photosynthesis/NPP product; and plant functional 

types according to the community land model being 

developed at NCAR for global climate modeling.

 The land cover dynamics product (MOD12Q2) 

quantifies subtle and progressive land-surface 

transformations as well as major rapid changes. As 

such, it is not a conventional change product that 

only compares changes in land cover type at two 

Terra / MODIS

Land Cover Image of Eastern Massachusetts. A close-up view of the global 

land cover product, made from the first 

consistent year of MODIS data, for eastern 

Massachusetts, U.S. Four primary IGBP 

vegetation classes are represented. Urban 

areas in the upper left and lower left are 

Boston and Providence, Rhode Island. 

Note the general transition from deciduous 

broadleaf forest on the west to mixed forest in southeastern Massachusetts and the 

Cape Cod Peninsula, where sandy glacial 

deposits favor a mix of deciduous trees 

with evergreen conifers. On the internet, 

visit visibleearth.nasa.gov to view a variety 

of MODIS data images.

IGBP Classes > 100 km2

0 Water

4 Deciduous Broadleaf Forest

5 Mixed Forests

13 Urban and Built-Up

14 Cropland/Natural Vegetation Mosaic

254 Other Classes and Unclassified
Vegetation Dynamics, Land Cover, and Land Cover Dynamics • Terra / MODIS Data Products Handbook - Volume 1 165

ness length, and surface resistance for surface energy 

balance models. 

Data Set Evolution

The MODIS land cover type product builds on the 

heritage of global vegetation cover data sets produced 

using the coarse spatial resolution and high-temporalfrequency data of the AVHRR instrument aboard the 

NOAA series of meteorological satellites. With these 

data, the information source is the annual sequence of 

composited Normalized Difference Vegetation Index 

(NDVI) values for each pixel. The NDVI responds to 

specifically to the proportion of the surface covered 

by green leaves as well as the thickness of the leaf 

layer. As such, it does not provide land cover directly. 

Rather, the timing and intensity of the annual greenup 

of the surface is the information used to determine 

land cover type. The MODIS land cover algorithm 

draws from information domains well beyond those 

of AVHRR NDVI. These domains include directional 

surface reflectance in seven spectral bands, near-infrared image texture, enhanced vegetation index, 

land-surface temperature, and snow/ice cover derived 

from the MODIS instruments on both Terra and Aqua. 

They are assembled for a year of observations for 

each 1-km pixel. The classification algorithm uses 

a decision-tree classifier trained from a network of 

1500 or more training sites. The validation procedure 

utilizes the training sites and classifier confidence to 

characterize the accuracy of the product as well as to 

provide information that can be used in spatial aggregation of land cover data and land cover change 

data at coarser resolutions.

Suggested Reading

Borak, J. S., and A. H. Strahler, 1999. 

Friedl, M. et al., 2000.

Friedl, M. A. et al., 1999.

Friedl, M. A., and C. E. Brodley, 1998.

Justice, C. et al., 1998.

Muchoney, D. et al., 1999.

Muchoney, D. et al., 1999.

Running, S. W. et al., 1994.

Running, S. W. et al., 1995.

Salisbury, J. W., and D. M. DʼAria, 1992.

Snyder, W., and Z. Wan, 1996.

Strahler, A. et al., 1995.

Wan, Z., and J. Dozier, 1996.

Wan, Z., and Z.-L. Li, 1997.

Zhang, X. et al., 2001. 

MODIS Land Cover Type Summary

Coverage: Global, clear-sky only

Spatial/Temporal Characteristics: 1 km and 

0.05°/annual (produced every 96 days)

Key Science Applications: Biogeochemical 

cycles, global climate modeling, land cover 

change

Key Geophysical Parameters: Land cover 

type, land cover change, vegetation phenology

Processing Level: 3

Product Type: Standard, post-launch

Maximum File Size:

MOD12Q1: 23 MB (1 km)

MOD12Q2: 23 MB (1 km)

Summary Products: 0.92 MB

File Frequency: 317/96 days

Primary Data Format: HDF-EOS

Additional Product Information: 

modis-land.gsfc.nasa.gov/products/products.a

sp?ProdFamID=10

geography.bu.edu/landcover

DAAC: EDC Land Processes DAAC,

edcdaac.usgs.gov

Science Team Contact: A. H. Strahler
166 Data Products Handbook - Volume 1 • Vegetation Dynamics, Land Cover, and Land Cover Dynamics 

• 1-km NDVI, EVI, and QA as 16-day and monthly 

products (standard resolution); and

• 25-km NDVI, EVI, and QA as 16-day and monthly 

products (coarse resolution)

Research and Applications

Due to their simplicity, ease of application, and widespread familiarity, vegetation indices are widely used 

by the broader user community from global circulation climate modelers and EOS instrument teams 

and interdisciplinary projects in hydrology, ecology, 

and biogeochemistry to those making regional- and 

global-based applications involving natural-resource 

inventories, land-use planning, agricultural monitoring and forecasting, and drought forecasting. Some 

of the more common applications of the vegetation 

index concern:

• Global warming/climate 

• Global biogeochemical and hydrologic modeling

• Agriculture; precision agriculture; crop stress, crop 

mapping

• Rangelands; water supply forecasting; grazing 

capacities; fuel supply

• Forestry, deforestation, and net primary production 

studies

• Pollution/health issues (Rift valley fever, mosquitoproducing rice fields)

• Desertification

• Anthropogenic-change detection and landscape 

disturbances.

Data Set Evolution

There is a 20+ year NDVI global data set (1981-

2002) from the NOAA-AVHRR series, which could 

be extended by MODIS Terra and Aqua data to provide a long-term data record for use in operational 

monitoring studies. The MODIS Terra and Aqua data 

set can readily be composited to provide 16-day, 

cloud-free time-series maps of vegetation activity. 

Combined, Terra and Aqua data will provide higher 

frequency, cloud-free time series data.

Terra / MODIS

MODIS Vegetation Indices 

(MOD13)

Product Description

The MODIS Vegetation-Index (VI) products will 

provide consistent spatial and temporal comparisons of global vegetation conditions that are used to 

monitor the Earthʼs terrestrial photosynthetic vegetation activity in support of change detection and 

phenologic and biophysical interpretations. Gridded 

vegetation-index maps depicting spatial and temporal 

variations in vegetation activity are derived at 16-

day and monthly intervals for precise seasonal and 

interannual monitoring of the Earthʼs vegetation. The 

MODIS VI products will improve upon currently 

available indices and will more accurately monitor 

and detect changes in the state and condition of the 

Earthʼs vegetative cover. The vegetation-index products are made globally robust with enhanced vegetation sensitivity and minimal variations associated 

with external influences (atmosphere, view and sun 

angles, clouds) and inherent, non-vegetation influences (canopy background, litter), in order to serve 

more effectively as a “precise” measure of spatial and 

temporal vegetation change.

 Two vegetation-index (VI) products are produced 

globally for land. One is the standard normalized difference vegetation index (NDVI), which is referred 

to as the “continuity index” to the existing NOAAAVHRR-derived NDVI. The other is an enhanced 

vegetation index (EVI) with improved sensitivity in 

high biomass regions and improved vegetation monitoring through a decoupling of the canopy background 

signal and a reduction in atmosphere influences. The 

two VIs complement each other in global vegetation 

studies and improve upon the extraction of canopy 

biophysical parameters. A new compositing scheme 

that reduces angular, sun-target-sensor variations is 

also utilized. The gridded vegetation-index maps use 

as input MODIS Terra and Aqua surface reflectances, 

corrected for molecular scattering, ozone absorption, 

and aerosols. The gridded vegetation indices include 

quality-assurance (QA) information with statistical 

data, indicating the quality of the VI product and 

input data. The MODIS vegetation-index products 

include:

• 250-m NDVI, EVI, and QA as 16-day products 

(high resolution);

• 500-m NDVI, EVI, and QA as 16-day products;
Vegetation Dynamics, Land Cover, and Land Cover Dynamics • Data Products Handbook - Volume 1 167

Suggested Reading

Cihlar, J. C. et al., 1997.

Gao, X. et al., 2000.

Gutman, G. G., 1998.

Huete, A. R. et al., 1994.

Huete, A. R. et al., 1997.

Huete, A. R. et al., 1999.

Myneni, R. B. et al., 1997a,b.

Pinty, B. et al., 1993.

van Leeuwen, W. J. D. et al., 1999.

MODIS Vegetation Indices Summary

Coverage: Global land surface

Spatial/Temporal Characteristics: 250-m, 

500- m, 1-km, and 0.25°-resolutions/16-day 

and monthly products for Terra and Aqua data 

separately.

Key Science Applications: Global vegetation 

monitoring, biogeochemical and hydrologic 

modeling, health and food security, range and 

forestry monitoring, agriculture management

Key Geophysical Parameters: Vegetation 

indices (NDVI & EVI)

Processing Level: 3

Product Type: Standard, at-launch

Maximum File Size: 277 MB

File Frequency: 286/16-day (16-day), 

289/month (Monthly)

Primary Data Format: HDF-EOS

Additional Product Information: 

modis-land.gsfc.nasa.gov/products/

products.asp?ProdFamID=6

DAAC: EDC Land Processes DAAC,

edcdaac.usgs.gov

Science Team Contact: A. R. Huete

 Terra / MODIS

MODIS Leaf Area Index 

(LAI) and Fraction of 

Photosynthetically Active 

Radiation (FPAR) – Moderate 

Resolution (MOD15)

Product Description

The MOD15 Leaf Area Index (LAI) and Fraction 

of Photosynthetically Active Radiation absorbed 

by vegetation (FPAR) are 1-km at-launch products 

provided on a daily and 8-day basis. LAI defines 

an important structural property of a plant canopy, 

namely the one-sided leaf area per unit ground area. 

FPAR measures the proportion of available radiation 

in the photosynthetically active wavelengths (400 to 

700 nm) that a canopy absorbs. The LAI product will 

be a LAI value between 0 and 8 of the global gridded 

database at the corresponding modified vegetation 

index (MVI) compositing interval. The FPAR product 

will be an FPAR value between 0.0 and 1.0 assigned 

to each 1-km cell of the global gridded database at 

the corresponding MVI compositing interval.

Research and Applications

LAI and FPAR are biophysical variables that describe 

canopy structure and are related to functional process 

rates of energy and mass exchange. Both LAI and 

FPAR have been used extensively as satellite- derived 

parameters for calculation of surface photosynthesis, 

evapotranspiration, and Net Primary Production 

(NPP). These products are essential in calculating 

terrestrial energy, carbon, water-cycle processes, and 

biogeochemistry of vegetation. The LAI product is 

an input to Biome-BGC (Biogeochemical) models to 

produce conversion-efficiency coefficients, which are 

combined with the FPAR product to produce daily 

terrestrial PSN (photosynthesis) and annual NPP.

Data Set Evolution

This product is derived from the Surface Reflectance Product (MOD09), the Land Cover Type 

product (MOD12), and ancillary information on 

surface characteristics such as land cover type and 

background. The retrievals are performed by comparing observed and modeled surface reflectances 

for a suite of canopy structures and soil patterns that 

covers a range of expected natural conditions. All 

canopy/soil patterns for which the magnitude of the 

residuals in the comparison does not exceed uncer-
168 Data Products Handbook - Volume 1 • Vegetation Dynamics, Land Cover, and Land Cover Dynamics 

tainties in observed and modeled surface reflectances 

are treated as acceptable solutions. For each acceptable solution, a value of FPAR is also evaluated. 

The mean and dispersion values of the LAI solution 

distribution function are taken as the retrieved LAI 

accuracy; likewise for FPAR. A three-dimensional 

formulation of the radiative transfer is used to derive spectral and angular biome-specific signatures 

of vegetation canopies. Should this main algorithm 

fail, a back-up algorithm is triggered to estimate LAI 

and FPAR using a Normalized Difference Vegetation 

Index (NDVI).

Suggested Reading

Knyazikhin, Y. et al., 1998a,b.

Myneni, R. B. et al., 1997b.

Tian, Y. et al., 2000.

Zhang, Y. et al., 2000.

Terra / MODIS

MODIS LAI and FPAR – Moderate 

Resolution Summary

Coverage: Global

Spatial/Temporal Characteristics: 1 km/daily,

8-day

Key Science Applications: Biogeochemical 

cycle modeling, NPP estimation

Key Geophysical Parameters: Leaf area index, 

fraction of photosynthetically active radiation 

absorbed by vegetation

Processing Level: 4

Product Type: Standard, at-launch 

Maximum File Size: 5.8 MB

File Frequency: 286/day (Daily Level 4),

289/8-day (8-day Level 4)

Primary Data Format: HDF-EOS 

Additional Product Information: 

modis-land.gsfc.nasa.gov/products/

products.asp?ProdFamID=5

DAAC: EDC Land Processes DAAC,

edcdaac.usgs.gov

Science Team Contacts: R. B. Myneni, 

S. W. Running

Global Leaf Area Index
Vegetation Dynamics, Land Cover, and Land Cover Dynamics • Terra / MODIS Data Products Handbook - Volume 1 169

MODIS Evapotranspiration 

(MOD16)

Editorʼs note: This product is only produced from MODIS on Aqua 

due to its afternoon equatorial crossing time. It is included in this 

volume for reference purposes.

Product Description

This Level 4 product is the ratio of evapotranspiration 

to potential evapotranspiration, expressed as an 

Evaporative Fraction (EF) to provide compatibility 

with both energy and hydrologic units. This land 

EF is an Aqua product that captures sub-optimum 

conditions produced by soil/atmosphere deficits for 

vegetation photosynthesis, transpiration and surface 

energy balances. It has a temporal resolution of 8 

days, at 1 km.

 Please note this product is only produced from 

MODIS on Aqua due to its afternoon equatorial 

crossing time. It is included in this volume for reference purposes.

Research and Applications

This new algorithm infers the fraction of incident 

solar energy being partitioned into Evaporation, 

effectively ET/PET. The algorithmʼs logic is to normalize the observed clear-sky radiometric surface 

temperature with NDVI. These two parameters are 

essential to global modeling of climate, water balance, and trace gases. In addition, they are required 

in estimating photosynthesis, respiration, and net primary production. The Evapotranspiration product is 

an Aqua (post-launch) research data product intended 

for real-time implementations. Practical applications 

of this product are for monitoring wildfire danger and 

crop/range drought.

Data Set Evolution

The Evapotranspiration product will be calculated using the MODIS Land Surface Temperature (MOD11) 

and the MODIS modified vegetation index (MOD13), 

and will be used along with incident radiation for 

computing the ET.

Suggested Reading

Goward, S. N., and A. S. Hope, 1989.

Nemani, R. R., and S. W. Running, 1989.

Nemani, R. R. et al., 1993.

Running, S. W. et al., 1989.

Running, S. W. et al., 1994.

MODIS Evapotranspiration Summary

Coverage: Global

Spatial/Temporal Characteristics: 1 km/8-day, 

yearly

Key Science Applications: Global water balance, net primary production

Key Geophysical Parameters: Evaporation, 

potential evaporation

Processing Level: 4

Product Type: Research, post-launch

Maximum File Size: 5.8 MB

File Frequency: 286/8-day or year

Primary Data Format: HDF-EOS

Additional Product Information:

modis-land.gsfc.nasa.gov/products/

products.asp?ProdFamID=3

DAAC: EDC Land Processes DAAC, 

edcdaac.usgs.gov

Science Team Contact: S. W. Running
170 Data Products Handbook - Volume 1 • Vegetation Dynamics, Land Cover, and Land Cover Dynamics 

MODIS Vegetation Production 

and Net Primary Production 

(MOD17)

Product Description

MOD17 is a Level 4 product consisting of 8-day Net 

Photosynthesis (PSN) and Net Primary Production 

(NPP). Annual NPP is the time integral of the PSN 

product over a year.

Research and Applications

This product provides an accurate measure of terrestrial vegetation growth and production activity. The 

theoretical use is to define the seasonally dynamic 

flux of terrestrial-surface carbon dioxide for climate 

modeling. Fluxes will be computed specific to each 

vegetation type. The practical utility is to measure 

crop yield and forest production and any other socially significant products of vegetation growth. As 

this global NPP product becomes regularly available, 

a wide variety of derived products is expected to be 

developed making regionally specific estimates of 

crop production. The value of an unbiased, regular 

source of crop and forest production estimates for 

global political and economic decision making is 

immense.

Data Set Evolution

The NPP parameter is the yearly integral of the PSN, 

which is obtained from the product of PAR (Photosynthetically Active Radiation), FPAR (Fraction of 

Photosynthetically Active Radiation) and conversionefficiency coefficients obtained from other MODIS 

products and other sensors and ancillary data. The 

algorithms for these products are based on the original 

logic of Monteith (1972), which relates PSN and NPP 

to the amount of Absorbed Photosynthetically Active 

Radiation (APAR). The MODIS modified vegetation 

indices (MVI) along with climate variables and the 

land cover product are used to estimate APAR.

Suggested Reading

Field, C. B. et al., 1995.

Monteith, J. L., 1972.

Nishida, K. et al., 2003.

Prince, S. D., and S. N. Goward, 1995.

Ruimy, A. et al., 1994.

Running, S. W., 1990.

Running, S. W. et al., 1994.

Running, S. W. et al., 2000.

MODIS Vegetation Production and Net 

Primary Production Summary

Coverage: Global

Spatial/Temporal Characteristics: 0.5, 1, 10 

km/8-day, yearly

Key Science Applications: Interannual variability of vegetation

Key Geophysical Parameters: NPP, photosynthesis, respiration

Processing Level: 4

Product Type: Standard, at-launch

Maximum File Size: 4.3 MB

File Frequency: 289/8-day

Primary Data Format: HDF-EOS

Additional Product Information:

modis-land.gsfc.nasa.gov/products/

products.asp?ProdFamID=3

DAAC: EDC Land Processes DAAC, 

edcdaac.usgs.gov

Science Team Contact: S. W. Running

 Terra / MODIS
Vegetation Dynamics, Land Cover, and Land Cover Dynamics • Terra / MODIS Data Products Handbook - Volume 1 171

Average Daily GPP

gC/m2/day

0 2 4 6 8 10 12 14

The above image represents Gross Primary Production (GPP) at 1 km resolution from MODIS using data collected from May 31 to 

June 7, 2001. Image courtesy of the MODIS Land Science Team/University of Montana. On the internet, visit visibleearth.nasa.gov 

to view a variety of MODIS data images.
172 Data Products Handbook - Volume 1 • Vegetation Dynamics, Land Cover, and Land Cover Dynamics 

ucts are provided as a 16 day composite. A 16-day 

summary albedo product at 0.05o spatial resolution is 

also provided. Both Terra and Aqua data are merged 

as available.

Research and Applications

The BRDF functions provided by the BRDF/Albedo 

parameter 1) allow normalization of MODIS data to 

standard viewing and illumination angles, thus removing geometric effects from multidate images; 2) 

quantify the directional information in the remotelysensed signal, which is related to ground-cover type; 

and 3) provide a surface-radiation-scattering model 

for boundary-layer parameterization in regional and 

global climate modeling. The BRDF can also be used 

in extraction of surface reflectances at Level 2. Two 

albedo measures are provided: “black-sky” albedo 

(directional-hemispherical reflectance) and “whitesky” albedo (bihemispherical reflectance). These 

are intrinsic surface properties, independent of atmospheric state. They describe the upward scattering 

of the solar beam and of uniform diffuse irradiance, 

respectively, and may be used as input to global and 

regional climate models.

MODIS Surface Reflectance 

BRDF/Albedo Parameter

(MOD43)

Product Description

The Bidirectional Reflectance Distribution Function 

(BRDF)/Albedo product provides coefficients for 

mathematical functions that describe the BRDF of 

each pixel in MODIS spectral “Land” bands (1-7), 

as well as for three broad bands (0.3-0.7, 0.7-5.0, 

and 0.4-3.0 μm).

 These coefficients are then used to provide albedo 

measures and nadir view-angle corrected surface reflectances resulting in three different file types, each 

containing global, 1km resolution data and quality 

assurance information:

• MOD43B1 contains three BRDF/Albedo model 

parameters and various quality assurance information.

• MOD43B3 contains both the white-sky (wholly 

diffuse) and black-sky (direct beam at local solar 

noon) albedo product.

• MOD43B4 contains Nadir BRDF-Adjusted Reflectances (NBAR) at the mean overpass solar angle.

Because deriving BRDF and albedo requires merging 

multiple looks at each pixel, the BRDF/Albedo prod135° W 120° W 105° W 90° W 75° W 60° W

120° W 105° W 90° W 75° W 60° W

30° N

30° N

0 1000 km

Albedo of the Sahara Desert and Arabian Peninsula. This image, prepared from MODIS data acquired in 

November 2000, shows the land surface albedo of the Sahara Desert and Arabian Peninsula. While global 

climate modelers typically assume a fixed albedo for barren deserts, the tonal variation demonstrates that 

desert albedos are actually quite variable and range from low, in regions of lava flows, to high, in sand sheet 

and dune fields. On the internet, visit visibleearth.nasa.gov to view a variety of MODIS data images.

Terra / MODIS
Vegetation Dynamics, Land Cover, and Land Cover Dynamics • Data Products Handbook - Volume 1 173

Data Set Evolution

The BRDF/Albedo algorithm combines gridded, multidate, multiband surface-reflectance data from EOS 

MODIS instruments on Terra and Aqua to produce 

BRDF functions and derived albedo measures. For 

each grid cell, all cloud-free observations in a 16-day 

period are assembled and fit to the Ross-Thick/LiSparse semiempirical model that describes the BRDF 

as a linear combination of two basic BRDF shapes 

and a constant. In addition, the Ross-Thick/Li-Transit model also fitted as a separate parameter. The 

algorithm outputs include 1) Ross-Thick/Li-Sparse 

BRDF parameters that best fit the observations, 2) 

Ross-Thick/Li-Transit parameters, 3) black-sky and 

white-sky albedos in three broad bands; and 4) nadir 

BRDF-adjusted reflectances (NBARs).

Suggested Reading

Barnsley, M. J., and J.-P. Muller, 1991.

dʼEntremont, R. E. et al., 1999.

Gao, F. et al., 2001.

Gao, F. et al., 2001.

Hu, B. et al., 1997.

Hu, B. et al., 1999.

Hu, B. et al., 2000.

Justice, C. et al., 1998.

Li, X. et al., 1995.

Li, X. et al., 2001.

Liang, S. et al., 1999

Lucht, W., 1998.

Lucht, W., and P. Lewis, 2000.

Lucht, W. et al., 2000.

Martonchik, J. V. et al., 2001.

Strugnell, N., and W. Lucht, 2001.

Strugnell, N. et al., 2001.

Wanner, W. et al., 1995.

Wanner, W. et al., 1997.

Terra / MODIS

MODIS Surface Reflectance BRDF/Albedo 

Parameter Summary

Coverage: Global land surface

Spatial/Temporal Characteristics: 1 km, 

0.05°/16-day

Key Science Applications: Biogeochemicalcycle modeling, net primary productivity estimation, global climate models

Key Geophysical Parameters: Bidirectional 

reflectance, spectral albedo

Processing Level: 3

Product Type: Standard, at-launch

Maximum File Size (per tile):

MOD43B1: 107 MB

MOD43B3: 66 MB

MOD43B4: 30 MB

File Frequency: 294/16-day

Primary Data Format: HDF-EOS

Additional Product Information: 

modis-land.gsfc.nasa.gov/products/

products.asp?ProdFamID=2

geography.bu.edu/brdf/

DAAC: EDC Land Processes DAAC, 

edcdaac.usgs.gov

Science Team Contacts: A. H. Strahler, 

J.-P. Muller
174 Data Products Handbook - Volume 1 • Vegetation Dynamics, Land Cover, and Land Cover Dynamics 

MODIS Vegetative Cover 

Conversion and Vegetation 

Continuous Fields (MOD44)

The MOD44 suite provides two products, which collectively characterize the extent of the Earthʼs vegetated land cover and changes in same. Both products 

use the unique combination of temporal and spectral 

resolution provided by MODIS to monitor the annual 

phenologic cycle of vegetation and to exploit this 

information to provide high quality products.

MOD44A: Vegetative Cover Conversion

Product Description

Using MODIS data from the two 250-m bands, 

the vegetative cover conversion product shows the 

global distribution of the occurrence of vegetation 

cover change. Where there is sufficient evidence, 

the type of change is labeled (e.g., forest conversion 

to agricultural fields or grassland or bare surface). 

The distribution of these changes is represented at 

a spatial resolution of 250 m and a gridded 5-km 

summary. These are generated at 32-day intervals 

depicting vegetation change since the corresponding 

period of the previous year. An enhanced product 

with a finer temporal resolution is currently being 

developed. The algorithm is designed as a conservative alarm product, which captures the spatial 

and temporal distribution of changes at global 

and regional scales. This information can in turn 

be used to focus on detected hotspots using finer 

resolution instruments.

Research and Applications

Vegetation-cover change is an important driver 

of many important biogeochemical, hydrological, 

and climate processes. It also represents the integrated response to several biophysical and anthropogenic impacts. Among the important influences 

of vegetation-cover change are the following:

• It strongly affects changes in many biophysical 

factors such as surface roughness and albedo;

• It has a major effect on changes in sensible heat 

flux, because it affects global albedo and surface 

roughness, which in turn affects atmospheric 

drag;

Terra / MODIS

• It is of crucial importance for determining the 

biogeochemical cycling of carbon, nitrogen, and 

other elements at regional-to-global scales;

• It has a major impact on the runoff characteristics 

of catchments through its effects on evapotranspiration and partitioning of precipitation into overland 

flow, interflow, and groundwater accretion; 

• It gives a direct insight into ecosystem response 

related to climate change and anthropogenic influences;

• It affects biodiversity through direct impacts on 

habitat; and

• It provides increasingly important information for 

natural-resource managers.

This product can be combined with data obtained 

from finer spatial resolution data from sensors such 

as ETM+ on Landsat 7 and ASTER on Terra to assist in the identification of the types of vegetation 

conversion occurring. The product also provides 

information to assist the acquisition strategy of finer 

resolution systems since it helps flag areas where 

significant changes are likely to be occurring.

 Changes of interest include flooding, burning, 

deforestation, agricultural expansion and urbanization.

This image shows change as a result of fire in Southern Georgia as 

detected by the MODIS Vegetation Cover Conversion. The fire occurred in May of 2002 and the changed area is depicted by black in 

the image. On the internet, visit visibleearth.nasa.gov to view a variety 

of MODIS data images.
Vegetation Dynamics, Land Cover, and Land Cover Dynamics • Terra / MODIS Data Products Handbook - Volume 1 175

Data Set Evolution

Previous work has shown that data with a resolution 

of 1 km and coarser are sufficient for the mapping 

of the distribution of vegetation cover and for the 

monitoring of those changes in vegetation cover 

caused by seasonal-to-interannual climate change. 

However, such relatively coarse resolution data 

are often inadequate to detect changes caused by 

anthropogenic factors and heterogeneous change 

events such as wildfires. Analyses of many types 

of vegetation-cover change indicate that they are 

relatively small in size largely due to the inherently 

local nature of anthropogenic vegetation-cover conversions. Consequently a very large proportion of 

changes is only detectable at fine spatial resolutions. 

For this reason it was decided to use the two 250-m 

bands for the identification and mapping of this type 

of conversion.

MOD44B: Vegetation Continuous Fields

Product Description

Vegetation Continuous Fields uses an annual database 

of 8-day surface reflectance observations to derive 

percent cover estimates at a spatial resolution of 500 

m. A gridded 5-km summary product is also available. 

The variables available include:

• Percent Tree Cover

• Percent Herbaceous Cover

• Percent Bare Cover

• Percent Needleleaf Vegetation

• Percent Broadleaf Vegetation

• Percent Evergreen Vegetation

• Percent Deciduous Vegetation

Continuous training using classified fine resolution data are employed to train the algorithm. A 

metrics-based approach is employed to capture the 

annual variation of vegetation characteristics. Example metrics include maximum greenness, minimum 

temperature, darkest observation and many others. 

Metrics were chosen based on a analysis to determine 

the best discriminating factors, which are not autocorrelated.

Research and Applications

The Vegetation Continuous Fields product represents 

a significant improvement over discrete land cover 

classifications because no a priori determinations 

are made as the the definitions of land cover classes. 

Modelers and resource managers can determine their 

own thresholds to determine what vegetation composition constitutes a “forest” or “savanna”. For those 

interested in estimating parameters such as surface 

roughness, the MOD44B product allows the user to 

estimate such a variable along a continuum of forest 

or other cover.

 Perhaps the most important application is providing a globally consistent estimate of forest cover 

which is repeatable and does not rely on the variety 

of definitions used within different administrative 

jurisdictions. The data can in turn then be used to 

0

80

This images represents MODIS Vegetation Continuous Fields tree cover product for 2001 with oceans in 

black and denser forests depicted in dark grey. On the internet, visit visibleearth.nasa.gov to view a variety 

of MODIS data images.
176 Data Products Handbook - Volume 1 • Vegetation Dynamics, Land Cover, and Land Cover Dynamics

estimate stocks of standing carbon. By comparing 

multiple annual MOD44B data sets it is possible 

to determine changes in forest extent and carbon 

inventory over time.

Data Set Evolution

This product has been extensively prototyped using 

the Advanced Very High Resolution Radiometer at 

scales of 1 km and 8 km. MODIS derived results 

show the unique capabilities of the instrument to 

better depict vegetation extent and characteristics. 

This is due to the increased spatial and spectral resolution as well as significantly improved calibration 

and geolocation.

Suggested Reading

DeFries, R. S. et al., 2000.

Hansen, M. C. et al., 2002.

Townshend, J. R. G., and C. O. Justice, 1988.

Zhan, X. et al., 2002.

MODIS Vegetative Cover Conversion and 

Vegetation Continuous Fields Summary

Coverage: Global, daytime

Spatial/Temporal Characteristics:

MOD44A - 250 m, 5 km/3-month, yearly

MOD44B - 500 m, 5 km/yearly

Key Geophysical Parameters:

MOD44A - Vegetation-cover change occurrence and type

MOD44B - Vegetation-cover extent and 

composition

Processing Level: 4

Product Type: Research, at-launch and postlaunch

Maximum File Size: 1 GB/tile (land tiles only)

File Frequency: 294 files/32-day

Primary Data Format: HDF-EOS

Browse Available:

modis.umiacs.umd.edu

Additional Product Information: 

modis.umiacs.umd.edu

DAAC: EDC Land Processes DAAC, 

edcdaac.usgs.gov

Science Team Contact: J. R. G. Townshend

Terra / MODIS
177

Chapter 11

Ocean Primary 

Productivity, 

Phytoplankton and 

Organic Matter

Terra

MODIS
178 Data Products Handbook - Volume 1 • Ocean Primary Productivity, Phytoplankton and Organic Matter 

Ocean Primary Productivity, Phytoplankton and 

Organic Matter – An Overview

Relationship to Global Change Issues

The global oceans, covering approximately 70% of 

the Earthʼs surface and extending in some locations to 

depths exceeding 10 km, contain a varied abundance 

of life forms; and central to the ocean ecosystem are 

the unicellular plants called phytoplankton. Phytoplankton take dissolved carbon dioxide from sea 

water and use it for photosynthesis, enabling phytoplankton growth and releasing oxygen. This process 

captures approximately the same amount of carbon as 

does photosynthesis by land vegetation. The amount 

and distribution of carbon dioxide consumed by the 

phytoplankton depends heavily on ocean circulation, 

which supplies nutrients to the upper layers of the 

ocean where sunlight is abundant. Some fraction of 

carbon produced by the phytoplankton sinks to the 

ocean floor—a long-term sink for atmospheric carbon 

dioxide. The fraction is not well known and is a source 

of a large uncertainty in the global carbon budget. 

 Changes in the amount of phytoplankton indicate 

changes in productivity of the oceans and provide a 

key ocean link for the monitoring of global climate 

change. Ocean and land biological productivity, heatexchange processes, and water-exchange processes 

are tightly coupled, and it is important for global 

change models to take such interactions into account in global climate-change prediction. The hope 

is that the phytoplankton and organic matter data 

products will help scientists to determine whether 

ocean productivity is changing and, if so, how such 

changes relate to the global carbon cycle and other 

global changes. 

Product Overview

The ten products discussed in this chapter are all 

from the MODIS instruments on the Terra and Aqua 

satellites, and include 25 different variables (sometimes called “parameters”) related to ocean biology 

and biogeochemistry. The products are grouped according to the Science Team members responsible 

for the algorithms. 

 Phytoplankton pigment or chlorophyll concentration has historically been the most widely used 

ocean color product. MODIS provides three Chlorophyll Concentration products, and two additional 

 Overview

Pigment Concentration products. Several of these are 

based on heritage algorithms to provide continuity 

with the CZCS and SeaWiFS data sets. Others differ with respect to whether they are for open ocean 

(Case 1) or coastal (Case 2) waters, and whether the 

algorithms are empirical or semi-analytical. The 

Pigment Concentration products (MOD19) and 

two associated products, Suspended Solids Concentration (MOD23) and Ocean Water Attenuation 

Coefficient (MOD26), are all based on empirical algorithms. Total Absorption Coefficients (MOD36), 

Dissolved Organic Matter Absorption (MOD24), 

and the phytoplankton absorption coefficient at 675 

nm are derived by inverting a radiance model, and a 

chlorophyll concentration (semi-analytic, MOD21) 

is then derived from the absorption at 675 nm. This 

relationship depends on the amount of chlorophyll per 

cell, which is related to the nutrient status (replete vs. 

deplete) as inferred from the SST (MOD28). 

 The Ocean Primary Productivity product (MOD27) 

provides the rate of net photosynthetic carbon fixation, a process that influences CO2

 in the atmosphere 

and ultimately global climate. Estimates are provided 

for the daily rate integrated over the euphotic zone 

and the mixed layer, averaged over 8-day periods, and 

an empirically derived estimate of annual euphotic 

production based on the mean annual chlorophyll 

concentration. MOD27 is a Level 4 product that 

is analogous to the Net Primary Production (NPP) 

product over land (MOD04). 

 MODIS introduces three products that have never 

been produced before from space. The Coccolith 

Concentration product (MOD25) describes the concentration of calcium carbonate scales produced by 

the phytoplankton group named coccolithophores. 

These scales (called coccoliths) eventually sink to 

the ocean floor, accounting for about 75% of the deposition of carbon on the sea floor (Holligan et al., 

1993; Archer and Maier-Reimer, 1994). Chlorophyll 

Fluorescence (MOD20) provides another measure of 

the chlorophyll concentration, but the fluorescence 

per unit of chlorophyll depends on the health of the 

phytoplankton. Thus the ratio of chlorophyll fluorescence (MOD20) to chlorophyll (MOD21) provides information that is useful in modeling primary 

productivity. Finally, Phycoerythrin Concentration 

(MOD31) provides information about the abundance 

of species containing phycoerythrin. Among these 

are certain species of cyanobacteria which have the 
Ocean Primary Productivity, Phytoplankton and Organic Matter • Data Products Handbook - Volume 1 179

ability to convert nitrogen (N2) into organic nitrogen, 

thus playing an important role in the global nitrogen 

cycle (Capone et al., 1997). 

Product Interdependency and 

Continuity with Heritage Data

All of the products described in this chapter require 

accurate water-leaving radiances (MOD18), which 

in turn require match-up data (MOD32) to provide 

coincident remote sensing and in situ radiance measurements. Some individual products require additional parameters. For example, the semi-analytic 

chlorophyll product (MOD21) uses SST (MOD28), 

and the primary productivity product requires chlorophyll (MOD21) and SST (MOD28). The Chlorophyll 

Fluorescence (MOD20) is normalized to the absorbed 

radiation by phytoplankton (MOD22) to obtain a fluorescence efficiency. The MODIS Pigment Concentration (MOD19) includes a CZCS-analog pigment 

concentration to provide a direct link to the series of 

measurements made by CZCS over the period from 

1978 to 1986. Likewise, a SeaWiFS-analog chlorophyll concentration (MOD21) provides continuity 

with the SeaWiFS chlorophyll product. NASAʼs 

Sensor Intercomparison and Merger for Biological 

and Interdisciplinary Oceanic Studies (SIMBIOS) 

Project is developing methods for merging MODIS 

data with data from the heritage ocean color missions 

and data from contemporary international space missions (e.g., MERIS and GLI). 

Suggested Reading

Archer, A., and E. Maier-Reimer, 1994.

Capone, D. G. et al., 1997

Esaias, W. E. et al., 1999.

Gordon, H. R., 1978.

Gordon, H. R., 1997.

Goyet, C., and P. G. Brewer, 1993.

Holligan, P. M. et al., 1993.

Iverson, R. L. et al., 2000.

Letelier, R. M. et al., 1997.

Sikes, S., and V. Fabry, 1994.

Yoder, J. A. et al., 1993.

Overview
180 Data Products Handbook - Volume 1 • Ocean Primary Productivity, Phytoplankton and Organic Matter 

MODIS Pigment Concentration 

(MOD19), Suspended Solids 

Concentration (MOD23), and 

Ocean Water Attenuation 

Coefficient (MOD26)

Product Description

This set of products provides estimates of the concentration of phytoplankton pigments and other 

particulate matter, as well as a measure of light attenuation which is governed by these materials. The 

Pigment Concentration product (MOD19) provides 

three derived fields: a Coastal Zone Color Scanner 

(CZCS)-analog pigment concentration based on 

fluorometric measurements of chlorophyll a plus 

phaepigment; chlorophyll concentration based on 

high-pressure liquid chromatography (HPLC); and 

the total pigment concentration, also based on HPLC. 

Two additional products derived by empirical algorithms are the Total Suspended Matter (MOD23), 

and the Diffuse Attenuation Coefficient at 490 nm 

(MOD26). Level 2 products are generated daily and 

used to create Level 3 daily, 8-day weekly, monthly, 

and yearly products.

Research and Applications

Optical properties of the open ocean are predominantly governed by phytoplankton, specifically by 

pigments contained in living cells and by organic 

materials generated by the phytoplankton. Where this 

is true, the waters are called Case 1 waters. In Case 

2 waters, generally found in coastal regions, optical 

properties are affected by substances that do not covary with phytoplankton, such as suspended sediments 

and dissolved organic matter derived from the land. 

The suite of materials found in Case 1 waters tend 

to covary systematically. Consequently, the concentration of these substances can be derived empirically 

from the spectral reflectance or “color” of the water. 

Likewise, the penetration depth of incoming solar 

radiation is influenced by the concentration of these 

materials. The Diffuse Attenuation Coefficient at 490 

nm (the wavelength of maximum light penetration in 

the open ocean) is inversely related to the optical (or 

e-folding) depth of light at this wavelength. Ninety 

percent of the water-leaving radiance originates from 

the upper optical depth, and the euphotic depth is ~4.6 

times the optical depth.

 Terra / MODIS

Global image of Chlor_MODIS (MOD19) for May 2001 revealing the concentration of phytoplankton chlorophyll a. The algorithm used 

for Chlor_MODIS is based on high-pressure liquid chromatography (HPLC) measurements. On the internet, visit visibleearth.nasa.gov 

to view a variety of MODIS data images.

0.05 0.1 0.2 0.5 1.0 2.0 5.0 10.0

Chl mg/m3
Ocean Primary Productivity, Phytoplankton and Organic Matter • Data Products Handbook - Volume 1 181

Data Set Evolution

The algorithms used to generate these products are 

based on empirical relationships, derived according 

to methods developed for the CZCS mission as described by Gordon and Clark (1980). The algorithms 

have been refined and adapted to the MODIS bands. 

In addition, several of the algorithms are now based 

on HPLC pigment measurements rather than the fluorometric measurements used during the CZCS era. 

The Diffuse Attenuation Coefficient at 490 (called 

K490) has historically been a standard product provided by the CZCS and SeaWiFS missions.

Suggested Reading

Esaias, W. E. et al., 1998.

Gordon, H. R., and D. K. Clark, 1980.

Gordon, H. R. et al., 1980.

Gordon, H. R., and A. Y. Morel, 1983.

Lorenzen, C. J., and S. W. Jeffrey, 1980.

Smith, R. C., and K. S. Baker, 1977.

Terra / MODIS

MODIS Pigment Concentration, Suspended Solids Concentration, and Ocean 

Water Attenuation Coefficient Product 

Summary

Coverage: Global ocean surface, clear-sky 

only

Spatial/Temporal Characteristics: 1 km/daily 

(Level 2); 4.6 km, 36 km, 1°/daily, 8-day, 

monthly, yearly (Level 3)

Key Science Applications: Ocean productivity, biogeochemical models

Key Geophysical Parameters: Phytoplankton 

pigment concentration, total suspended matter concentration, light attenuation

Processing Level: 2, 3

Product Type: Standard, at-launch

Maximum File Size: 

MOD19: 102 MB (Level 2); 620 MB 

(Level 3)

MOD23: 102 MB (Level 2); 640 MB binned, 

134 MB mapped (Level 3)

MOD26: 102 MB (Level 2); 640 MB binned, 

134 MB mapped (Level 3)

File Frequency: 

MOD19: 144/day (Daily Level 2); 3/day (Daily Level 3), 3/8-day (8-day Level 3), 3/month 

(Monthly Level 3), 3/year (Yearly Level 3)

MOD23, MOD26: 144/day (Daily Level 2); 

1/day (Daily Level 3), 1/8-day (8-day Level 

3), 1/month (Monthly Level 3), 1/year (Yearly 

Level 3)

Primary Data Format: HDF-EOS

Browse Available: 36-km sample imagery 

available at the GES DAAC (Level 3 only)

Additional Product Information: 

modis-ocean.gsfc.nasa.gov/dataprod.html

DAAC: Goddard Space Flight Center Earth 

Sciences DAAC, daac.gsfc.nasa.gov

Science Team Contact: D. Clark
182 Data Products Handbook - Volume 1 • Ocean Primary Productivity, Phytoplankton and Organic Matter 

MODIS Chlorophyll 

Fluorescence (MOD20)

Product Description

This product contains three parameters describing 

ocean chlorophyll fluorescence properties. Fluorescence Line Height is a relative measure of the 

amount of radiance leaving the sea surface at the 

fluorescence wavelength of 683 nm. Fluorescence Efficiency provides a relative measure of the absorption 

of PAR and its emission as chlorophyll fluorescence. 

Fluorescence Baseline is the level above which the 

Line Height is calculated, determined by interpolating between adjacent MODIS bands. The Level 2 

product is produced daily at 1 km spatial resolution, 

and Level 3 is gridded and produced daily, 8-day 

weekly, monthly, and yearly.

Research and Applications

Solar-stimulated chlorophyll fluorescence is a measure of the current photophysiology of phytoplankton, 

in contrast to the biomass estimate provided by chlorophyll. The product quantifies the level of photosynthesis by phytoplankton in the ocean. Historically, 

the coupling between fluorescence and chlorophyll 

has been studied extensively, and recent research has 

focused on the use of Sun-stimulated fluorescence to 

estimate primary productivity (Kiefer and Reynolds, 

1992). Basic fluorometric measurements are made 

using an instrument described by Holm-Hansen et al.

(1965) that uses blue-light stimulation; this method 

has been used unchanged for 30 years. Gower and 

Borstad (1990) were among the first to attempt to 

use solar-stimulated fluorescence at 683 nm to estimate chlorophyll concentrations from aircraft and 

satellites. 

Data Set Evolution

Inputs to the algorithm are Water-Leaving Radiance 

(MOD18) in MODIS bands 13 (667 nm), 14 (678 

nm), and 15 (748 nm), and the Absorbed Radiation 

by Phytoplankton (ARP) (MOD22). The algorithm is 

applied to the daily input standard-product data sets 

and is remapped into standard Level 3 grids. The validation approach is to compare the fluorescence results 

with other MODIS data products (e.g., Chlorophyll a

concentration), with surface measurements, and with 

other satellite-based estimates of the same products. 

The products are produced only for non-cloud, glintfree ocean pixels during daylight hours. 

Suggested Reading

Abbott, M. R. et al., 1982.

Abbott, M. R., and R. M. Letelier, 1998.

Chamberlin, W. S., and J. Marra, 1992.

Esaias, W. E. et al., 1998.

Gower, F. J. R., and G. A. Borstad, 1990.

Hoge, F. E. et al., 2003.

Holm-Hansen, O. et al., 1965.

Kiefer, D. A., and R. A. Reynolds, 1992.

Letelier, R. M. et al., 1997.

Topliss, B. J., and T. Platt, 1986.

MODIS Chlorophyll Fluorescence Summary

Coverage: Global ocean surface, clear-sky 

only

Spatial/Temporal Characteristics: 1 km for 

chlorophyll levels greater than 2.0 mg/m3/

daily, (Level 2); 4.6 km, 36 km, 1°/8-day, 

monthly, yearly (Level 3)

Key Science Applications: Ocean chlorophyll, ocean productivity

Key Geophysical Parameters: Chlorophyll 

fluorescence (fluorescence line height, 

fluorescence efficiency, fluorescence line 

curvature)

Processing Level: 2, 3

Product Type: Standard, at-launch

Maximum File Size: 102 MB (Level 2); 640 

MB binned, 134 MB mapped (Level 3) 

File Frequency: 144/day (Daily Level 2); 

3/day (Daily Level 3), 3/week (Weekly Level 

3), 3/month (Monthly Level 3), 3/year (Yearly 

Level 3)

Primary Data Format: HDF-EOS

Additional Product Information:

modis-ocean.gsfc.nasa.gov/dataprod.html

DAAC: Goddard Space Flight Center Earth 

Sciences DAAC, daac.gsfc.nasa.gov

Science Team Contacts: M. Abbott, 

R. Letelier

 Terra / MODIS
Ocean Primary Productivity, Phytoplankton and Organic Matter • Terra / MODIS Data Products Handbook - Volume 1 183

Chlorophyll a Concentrations (MOD21) as measured by MODIS 

in the Indian Ocean on March 1, 2000. Chlorophyll, the green 

pigment in plants and phytoplankton, absorbs sunlight for use 

in photosynthesis. Note the high concentrations (light grays) in 

the Arabian Sea and relatively low concentrations (dark grays) in 

the Bay of Bengal. The black color in the image represents land 

areas and gaps between successive satellite orbits, and the gray 

“stripes” indicate where clouds or sun glint prevented accurate 

measurements.

Chlorophyll a (Chl)

Chlorophyll Fluorescence (MOD20) as measured from the Terra 

MODIS instrument on March 1, 2000. Phytoplankton re-emit 

some of the light that is captured by chlorophyll as fluorescence. 

The amount of fluorescence is roughly proportional to the amount 

of chlorophyll, so in this image, for example, high fluorescence 

can be found in the Arabian Sea.

Fluorescence Line Height (FLH)

Ratio of Chlorophyll Fluorescence (MOD20) to Chlorophyll a

Concentration (MOD21), illustrating the considerable variability 

in the amount of fluorescence per unit of chlorophyll. This variability is largely a function of the health of the phytoplankton. 

A high ratio implies lower growth rates, as the light captured by 

phytoplankton is being re-emitted as fluorescence rather than 

being used for photosynthesis. Phytoplankton in the Arabian Sea 

are probably growing more rapidly than elsewhere, perhaps in 

response to dust inputs (which are rich in iron) from the Arabian 

Peninsula.

FLH/Chl Ratio
184 Data Products Handbook - Volume 1 Terra / MODIS • Ocean Primary Productivity, Phytoplankton and Organic Matter 

MODIS Chlorophyll a Pigment 

Concentration (MOD21), 

Organic Matter Concentration 

(MOD24), and Absorption 

Coefficients (MOD36)

Product Description

The MODIS Chlorophyll a Pigment Concentration product (MOD21) provides two estimates of 

the concentration of chlorophyll a: Chlor_a_2 and 

Chlor_a_3. Chlor_a_2 is a SeaWiFS analog derived 

according to the OC3M algorithm as documented 

by OʼReilly et al., 2000. This is based on an empirical algorithm, and is intended to provide continuity 

with the SeaWiFS chlorophyll product. Chlor_a_3 

is derived from a semi-analytic algorithm for Case 1 

and Case 2 waters. This involves the inversion of a radiance model to determine the absorption coefficient 

due to phytoplankton at 675 nm, and the absorption 

coefficient of chromophoric dissolved organic matter 

(CDOM, also called yellow substance or gelbstoff) 

at 400 nm. Absorption coefficients generated by this 

algorithm are also provided. The Absorption Coefficient product (MOD36) includes phytoplankton 

absorption at 675 nm, and total absorption at 412, 

443, 488, 532, and 551 nm. The CDOM absorption 

at 400 nm is MOD24. The Level 2 products are produced daily at 1-km resolution, and then remapped 

to derive Level 3 products for daily, 8-day weekly, 

monthly, and yearly periods. Valid data exist only for 

cloud-free ocean pixels. The weekly and longer term 

composites are the average of cloud-free acquisitions 

for each ocean pixel. 

Research and Applications

Chlorophyll a is the light-harvesting pigment found in 

all photosynthetic plants. Its concentration is widely 

used as an index of phytoplankton biomass and, as 

such, is a key input to primary productivity models. 

When converted to carbon or nitrogen, it is used 

in modeling biogeochemical cycles. On short time 

scales, chlorophyll can be used to trace oceanographic 

currents, jets, and plumes. The 1-km resolution and 

nearly daily coverage of the MODIS data thus allows 

the observation of meso-scale oceanographic features 

in coastal and estuarine environments, which are of 

increasing importance in marine science studies (Liu 

et al., 2002). Absorption coefficients can be used to 

estimate the solar heating rate of the upper ocean 

mixed layer, and to study the variability of this rate 

attributable to variations in phytoplankton absorption. The importance of CDOM as another constituent 

absorbing radiant energy is a topic ongoing research 

(Garver and Siegel, 1997).

Data Set Evolution

The Chlor_a_2 algorithm (OC3M) was parameterized using the same data set used to parameterize the SeaWiFS chlorophyll algorithm (OC4.v4). 

It consists of over 2,800 in situ measurements of 

reflectance and chlorophyll in Case 1 waters worldwide. The empirical OC3M algorithm depends on a 

predictable relationship between the waterʼs optical 

variability and its chlorophyll concentration. Thus 

it is suitable only for Case 1 waters. To derive the 

chlorophyll concentration in Case 2 waters, one must 

simultaneously solve for the effects of substances 

(e.g., CDOM) which do not covary with chlorophyll 

(for definitions of Case 1 and Case 2, see MOD19 

product description). This is the rationale for developing semi-analytic algorithms such as that used by 

Chlor_a_3. 

 Chlor_a_3 requires Water-Leaving Radiance 

(MOD18) and SST (MOD28) as input, and the 

Absorption Coefficients (MOD36) generated as an 

intermediate product. The relationship between absorption and the concentration of chlorophyll depends 

on the amount of “packaging” of chlorophyll in the 

cells. Packaging is affected by species composition 

(and size), exposure to light, nutrient conditions, and 

other factors. The SST (MOD28) is used to determine 

the relationship between the chlorophyll concentration and the absorption at 670 nm. This algorithm 

has been thoroughly tested using SeaWiFS data, and 

post-launch validation has been conducted using data 

from validation cruises. In addition, the algorithm has 

been tested using the same data used to parameterize 

the SeaWiFS at-launch chlorophyll algorithm (OC2). 

Chlor_a_3 is a principal input to the Ocean Primary 

Productivity product (MOD27).
Ocean Primary Productivity, Phytoplankton and Organic Matter • Terra / MODIS Data Products Handbook - Volume 1 185

Suggested Reading

Austin, R. W., 1974.

Carder, K. L. et al., 1986.

Carder, K. L. et al., 1991a,b.

Carder, K. L. et al., 1999.

Carder, K. L. et al., 2003.

Esaias, W. E. et al., 1998.

Holm-Hansen, O., and B. Riemann, 1978.

Lee, Z. P. et al., 1996.

OʼReilly, J. J. et al., 2000.

Smith, R. C., and K. S. Baker, 1982.

MODIS Chlorophyll a Pigment Concentration, Organic Matter Concentration, 

and Absorption Product Summary

Coverage: Global ocean surface, clear-sky 

only

Spatial/Temporal Characteristics: 1 km/daily 

(Level 2); 4.6 km, 36 km, 1°/daily, 8-day, 

monthly, yearly (Level 3)

Key Science Applications: Ocean productivity, bio-optical properties

Key Geophysical Parameters: Case 1 and 

2 chlorophyll a concentration, absorption 

coefficients

Processing Level: 2, 3

Product Type: Standard, at-launch

Maximum File Size: 

MOD21: 83 MB (Level 2); 640 MB binned, 

134 MB mapped (Level 3)

MOD24: 83 MB (Level 2); 640 MB binned, 

134 MB mapped (Level 3)

MOD36: 102 MB (Level 2); 865 MB binned, 

134 MB mapped (Level 3)

File Frequency: 

MOD21 and MOD36: 144/day (Daily Level 

2); 2/day (Daily Level 3), 2/8-day (8-day 

Level 3), 2/month (Monthly Level 3), 2/year 

(Yearly Level 3)

MOD24: 144/day (Daily Level 2); 1/day (Daily Level 3), 1/8-day (8-day Level 3), 1/month 

(Monthly Level 3), 1/year (Yearly Level 3)

Primary Data Format: HDF-EOS

Browse Available: 36 km sample imagery 

available at the GES DAAC (Level 3 only)

Additional Product Information:

modis-ocean.gsfc.nasa.gov/dataprod.html

DAAC: Goddard Space Flight Center Earth 

Sciences DAAC, daac.gsfc.nasa.gov

Science Team Contacts: K. Carder,

J. Campbell
186 Data Products Handbook - Volume 1 • Ocean Primary Productivity, Phytoplankton and Organic Matter 

Global images of chlorophyll concentration for May 2001 derived from MODIS Terra data (MOD21). The Chlor_a_2 product (top) is 

based on an empirical band-ratio algorithm (OC3M) designed to be consistent with the SeaWiFS chlorophyll algorithm (OC4). The 

Chlor_a_3 chlorophyll concentration (bottom) is derived from a semi-analytic algorithm which accounts for the effects of pigment 

packaging. This effect is especially pronounced in the Southern Ocean where Chlor_a_3 indicates higher chlorophyll levels than the 

empirical (ratio) algorithms used by SeaWiFS and Chlor_a_2. Chlor_a_3 is used in deriving primary productivity (MOD27). On the 

internet, visit visibleearth.nasa.gov to view a variety of MODIS data images.

0.05 0.1 0.2 0.5 1.0 2.0 5.0 10.0

Chl mg/m3

Terra / MODIS
Ocean Primary Productivity, Phytoplankton and Organic Matter • Data Products Handbook - Volume 1 187

MODIS Coccolith 

Concentration (MOD25)

Product Description

This Level 2 and 3 product provides five parameters describing the concentration of coccoliths in 

sea water: the detached coccolith concentration in 

number/m3; the estimated calcite concentration due to 

the coccoliths in mg-CaCO3/m3; the pigment concentration in the coccolithophore biomass; a descriptor 

for the particular look-up table used; and a quality 

measure. The product is produced at 1-km spatial 

resolution daily for Level 2 and at 4.6-km, 36-km, 

and 1° resolution daily, 8-day weekly, monthly, and 

yearly for Level 3.

Research and Applications

Coccolithophores are small marine phytoplankton, 

which form external calcium carbonate (CaCO3) 

scales (called coccoliths) having diameters of a few 

mm and a thickness of 250 to 750 nm. Coccolithophores are the largest source of calcium carbonate 

on Earth. Thus, coccolith production is an important 

part of the biogenic carbon cycle. The observed 

characteristics of coccolithophores, including their 

ubiquitous nature, possible role in climate, and intense scattering property, make a global-scale study of 

their distribution an important application for MODIS 

imagery. Specifically, it is important to estimate the 

rate at which CaCO3 is formed by phytoplankton and 

to look for long-term changes in that rate.

Data Set Evolution

The algorithm for extracting the detached coccolith 

concentration from surface waters is based on the 

semianalytic model of ocean color of Gordon (1988). 

The model relates the normalized water-leaving radiance to the absorption and scattering properties of 

the constituents of the water using radiative-transfer 

theory. The absorption and scattering properties are 

then related to the constituent concentrations through 

statistical analysis of direct measurements. The model 

is validated by comparison with a set of water-leaving 

radiances independent of the measurements used to 

establish the statistical relationships between constituents and optical properties.

Terra / MODIS

Suggested Reading

Balch, W. M. et al., 1991.

Balch, W. M. et al., 1996a,b.

Balch, W. M. et al., 1999.

Balch, W. M. et al., 2001.

Gordon, H. R. et al., 1988. 

Holligan, P. M. et al., 1983.

Sarmiento, J. L. et al., 1988.

Sikes, S., and V. Fabry, 1994.

MODIS Coccolith Concentration 

Summary

Coverage: Global ocean surface, clear-sky 

only

Spatial/Temporal Characteristics: 1 km/daily 

(Level 2); 4.6 km, 36 km, 1°/daily, 8-day, 

monthly, yearly (Level 3)

Key Science Applications: Input to global 

biogeochemical cycle models

Key Geophysical Parameters: Coccolith and 

calcite concentration, pigment concentration 

in coccolithophore blooms

Processing Level: 2, 3

Product Type: Standard, at-launch

Maximum File Size: 102 MB (Level 2); 

640 MB binned, 134 MB mapped 

(Level 3)

File Frequency: 144/day (Daily Level 2); 3/

day (Daily Level 3), 3/8-day (8-day Level 3), 

3/month (Monthly Level 3), 3/year (Yearly 

Level 3)

Primary Data Format: HDF-EOS

Browse Available: 36 km sample imagery 

available at the GES DAAC (Level 3 only)

Additional Product Information:

modis-ocean.gsfc.nasa.gov/dataprod.html

DAAC: Goddard Space Flight Center Earth 

Sciences DAAC, daac.gsfc.nasa.gov

Science Team Contacts: H. R. Gordon,

W. B. Balch
188 Data Products Handbook - Volume 1 Terra / MODIS • Ocean Primary Productivity, Phytoplankton and Organic Matter 

Calcite concentration image of the Gulf of Maine on July 11, 2002. Satellite ocean-color imagery, in 

addition to allowing the concentration of phytoplankton pigments to be measured remotely, permits 

the detection and identification of certain algal blooms. The white-to-light-grey waters over the deep 

basins in the Gulf of Maine are elevated levels of calcite produced by a single type of algae (called 

coccolithophores), that affects regional climate and fisheries. The apparent high calcite levels over 

Georges Bank (lower right) are artifacts that occur over shallow regions that are high in suspended 

sediment. On the internet, visit visibleearth.nasa.gov to view a variety of MODIS data images.

Maine

Cape Cod

Nova

Scotia

25.00

20.00

15.00

10.00

5.00

0.00

mgC/m3
Ocean Primary Productivity, Phytoplankton and Organic Matter • Terra / MODIS Data Products Handbook - Volume 1 189

MODIS Ocean Primary 

Productivity (MOD27)

Product Description

This Level 4 product provides estimates of the Ocean 

Primary Productivity on an 8-day and an annual basis 

at spatial resolutions of 4.6 km and 36 km. There are 

two 8-day productivity estimates in units of gC/m2d: 

One (P1) is based on the Behrenfeld and Falkowski 

(1997a) model which provides primary productivity 

within the euphotic zone (above the 1% light depth), 

and the other (P2) is based on the model of Howard 

and Yoder (1997), which represents productivity 

within the upper mixed layer. The annual primary 

productivity, in units of gC/m2y, is derived using 

an empirical algorithm based on the annual mean 

chlorophyll concentration as described in Iverson et 

al. (2000). 

 

Research and Applications

The objective of the product is to quantify the magnitude and interannual variability (for decadal trends) in 

the oceanic primary productivity and phytoplankton 

carbon fixation. Primary productivity is the time rate 

of change of phytoplankton biomass, and, with allowance for excreted soluble carbon compounds, reflects 

the daily integrated photosynthesis within the water 

column. The integral of the values over the year is the 

annual primary productivity. The annual productivity 

product will be used for global-and regional-scale 

studies of interannual variability of ocean productivity, for comparisons with annual summations of 

short-term (8-day) analytical estimates, and for comparison with global biogeochemical models (Esaias et 

al., 1999). Cloudiness prevents deriving chlorophyll 

a concentrations over about 50% of the ocean on a 

daily basis, excluding that already lost due to high 

sun glint. Chlorophyll a concentrations derived from 

all available sensors, including the Terra and Aqua 

MODIS instruments, will be used to increase sampling frequency since these plankton processes vary 

rapidly over time and space. 

Data Set Evolution

Ocean primary productivity algorithms fall into two 

general classes, termed empirical and analytical. The 

empirical approach is based on a simple linear correlation between time-averaged in situ estimates of 

productivity and satellite-derived estimates of surface 

chlorophyll concentration in regions of the ocean 

where there are seasonal phytoplankton blooms. 

The analytical approach is based on models of the 

general photosynthetic response of the algal biomass 

as a function of major environmental variables such 

as light, temperature, and nutrient concentration. The 

overall methodologies differ significantly in the way 

various parameters are estimated and in the way they 

are assigned spatially and temporally across ocean 

basins (Behrenfeld and Falkowski, 1997b). The 

approach taken for the MODIS algorithm was to 

implement the annual empirical algorithm, and two 

analytical algorithms which were selected as part of a 

primary productivity algorithm round robin (PPARR) 

experiment (Campbell et al., 2002). 

 The PPARR experiment involved the comparison 

of 12 algorithms developed by 10 teams. It was undertaken with the goal of developing a consensus 

analytical algorithm for short-term (daily to weekly) 

global productivity. The two analytical algorithms 

used for this MODIS product were among the best 

performing algorithms. When compared with in situ

data at 89 globally distributed stations, their retrievals 

were within a factor of two of the depth-integrated 

primary productivity estimated using 14C incubation 

techniques. Temporally and areally integrated production is expected to reduce the standard error, 

according to statistical principles, but this cannot be 

tested because the only means of obtaining basin- or 

global-scale estimates of oceanic productivity is by 

satellite observations. 

 This Level 4 product uses the Chlor_a_3 chlorophyll concentration from MOD21 and the D1 

sea surface temperature from MOD28. The photosynthetically available radiation (PAR) is obtained 

from the GSFC Data Assimilation Office (DAO) by 

the GES DAAC, and mixed layer depth (MLD) is 

obtained from the Navyʼs Fleet Numerical Monterey 

Oceanographic Center via NOAA NESDIS. These 

ancillary data fields used in the calculations for 

primary productivity are provided with the MOD27 

product and are also archived and available from the 

GES DAAC.
190 Data Products Handbook - Volume 1 Terra / MODIS • Ocean Primary Productivity, Phytoplankton and Organic Matter 

Suggested Reading

Behrenfeld, M. J., and P. G. Falkowski, 1997a,b.

Campbell, J. W. et al., 2002.

Eppley, R. W. et al., 1985.

Esaias, W. E. et al., 1998.

Esaias, W. E. et al., 1999.

Falkowski, P. G., and J. A. Raven, 1997.

Fitzwater, S. E. et al., 1982.

Iverson, R. L. et al., 2000.

Morel, A., and J. M. Andre, 1991.

Platt, T. C. et al., 1991.

MODIS Ocean Primary Productivity 

Summary

Coverage: Global ocean surface, clear-sky 

only

Spatial/Temporal Characteristics: 4.6 km and 

36 km/8-day and a running annual average 

Key Science Applications: Ocean productivity, biogeochemical models

Key Geophysical Parameters: Annual and 

weekly ocean productivity

Processing Level: 4

Product Type: Standard, at-launch

Maximum File Size: 1200 MB binned 

(8-day), 1360 MB binned (Yearly); 32 MB 

mapped (8-day and Yearly)

File Frequency: 4/8-day

Primary Data Format: HDF-EOS

Additional Product Information: 

modis-ocean.gsfc.nasa.gov/dataprod.html

DAAC: Goddard Space Flight Center Earth 

Sciences DAAC, daac.gsfc.nasa.gov

Science Team Contact: W. Esaias

MODIS Phycoerythrin 

Concentration (MOD31)

Product Description

This product consists of two parameters which give 

the concentration of one of the major algal pigment 

groups in ocean water, Phycoerythrin. The two 

parameters are Phycoerythrobilin Concentration 

and Phycourobilin Concentration. These quantities 

are provided for Level 2 at 1-km spatial resolution 

daily and for Level 3 daily, 8-day weekly, monthly, 

and yearly at 4.6-km, 36-km, and 1° resolution. The 

product is valid only for clear-sky ocean pixels.

Research and Applications

One of the intended uses of the phycoerythrin data 

product is to allow scientific investigators to study 

the global distributions of the phycoerythrin pigment 

and, in so doing, to allow definition of the diversity 

of phycoerythrin-bearing species such as cyanobacteria. Phycoerythrin is one of three major algal 

pigment groups found in marine phytoplankton and 

bacteria (Bidigare et al., 1990). The phycoerythrins 

are subdivided into phycourobilin-rich (PUB) and 

phycoerythrobilin-rich (PEB) phycoerythrins. This 

algorithm retrieves both PUB- and PEB-rich cases. 

Phycoerythrin is a chlorophyll accessory pigment and 

serves to receive photosynthetically usable light in 

the 480-505 nm and 540-560 nm ranges. It is used 

to infer the global extent of phycoerythrin-bearing 

phytoplankton such as cyanobacteria, some of which 

are nitrogen-fixing and thus provide information on 

the nitrogen cycle. Used in conjunction with phytoplankton chlorophyll a and coccolithophore distributions, the apparent species diversity of the oceans 

can be inferred.

Data Set Evolution

The phycoerythrin retrieval algorithm requires 

water-leaving radiances (MOD18) and involves 

the inversion of a semi-analytical radiance model. 

In the forward direction, the model (Gordon et al., 

1988) predicts the radiance from the incident solar 

irradiance, the total backscatter, and the total absorption of sea water. The PUB and PEB parameters are 

retrieved by inverting the model using a sequentialconvergent-iteration method applied to the first five 

MODIS ocean bands (bands 8-12). MODIS band 10 
Ocean Primary Productivity, Phytoplankton and Organic Matter • Data Products Handbook - Volume 1 191

MODIS Phycoerythrin Concentration 

Summary

Coverage: Global ocean surface, clear-sky 

only

Spatial/Temporal Characteristics: 1 km/daily 

(Level 2); 4.6 km, 36 km, 1°/daily, 8-day, 

monthly, yearly (Level 3)

Key Science Applications: Global phytoplankton species studies, ocean productivity 

models

Key Geophysical Parameters: Phycoerythrobilin-rich (PEB) and phycourobulin-rich 

(PUB) phycoerythrins

Processing Level: 2, 3

Product Type: Research, at-launch

Maximum File Size: 102 MB (Level 2); 640 

MB binned, 134 MB mapped (Level 3)

File Frequency: 144/day (Daily Level 2); 2/

day (Daily Level 3), 2/8-day (8-day Level 3), 

2/month (Monthly Level 3), 2/year (Yearly 

Level 3)

Primary Data Format: HDF-EOS

Browse Available: 36-km sample imagery 

available at the GES DAAC (Level 3 only)

Additional Product Information: 

modis-ocean.gsfc.nasa.gov/dataprod.html

DAAC: Goddard Space Flight Center Earth 

Sciences DAAC, daac.gsfc.nasa.gov

Science Team Contact: F. E. Hoge

Terra / MODIS

(488 nm) and band 12 (551 nm) correspond to the 

peaks of the PUB and PEB phycoerythrins. The major assumption for the algorithm is that the pigmentspecific absorption-coefficient spectral model used is 

applicable for the oceanic province where the satellite 

image was acquired. The algorithm will be validated 

by ship and airborne laser-induced and water Ramannormalized fluorescence measurements. 

Suggested Reading

Bidigare, R. R. et al., 1990.

Culver, M. E., and M. J. Perry, 1994.

Esaias, W. E. et al., 1998.

Gordon, H. R. et al., 1988.

Hoge, F. E., and R. N. Swift, 1986.

Hoge, F. E., and R. N. Swift, 1990.

Hoge, F. E. et al., 1999a,b.
193

Chapter 12

Snow and Ice Cover

Terra

MODIS
194 Data Products Handbook - Volume 1 • Snow and Ice Cover 

Snow and Ice Cover – An Overview

Relationship to Global Change Issues

 Overview

Snow and ice cover dramatically influence surface 

albedo, hydrologic processes, the interactions between the atmosphere and the Earthʼs surface, and 

regulation of ecosystem biological activity. For instance, when sea ice is formed in the ocean or snow 

is deposited on land, there is a dramatic increase in 

the surface reflectance, and a decrease in the amount 

of solar energy which is absorbed, hence affecting 

the Earthʼs radiation balance and, in turn, its climate. 

Furthermore, both snow and ice are highly variable 

components of the climate system, changing dramatically from summer to winter, and, to a lesser extent, 

from year to year and thus dramatically influence the 

Earthʼs energy balance. Two key sensors that provide 

enhanced global sea ice and snow measurements to 

supplement existing data sets are the AMSR-E on 

Aqua and MODIS. The MODIS is onboard both the 

Terra and Aqua satellites. Both instruments provide 

information to be used in the development of more accurate models to help understand and predict Earthʼs 

climate. MODIS has been providing daily, global coverage of sea ice and snow under clear-sky conditions, 

and sea ice extent and ice-surface temperature (IST) 

during both daylight and darkness. Sea ice and snow 

maps are used for climate research. By extending the 

existing time series of sea ice and snow cover, which 

currently spans over 20 years, scientists expect to 

obtain a better understanding of global, hemispheric, 

and regional variables on seasonal, interannual, and 

decadal time scales. Monthly special products are 

used in general circulation modeling (GCM) for 

validation of their models.

 Sea ice is present over approximately 7% of the 

Earthʼs ocean surface (Washington and Parkinson, 

1986). Snow-covered sea ice, with its high albedo, 

is a key parameter of the global energy balance, 

reflecting much of the incident solar radiation back 

to space. Additionally, sea ice cover is an insulating 

layer between the ocean and atmosphere; heat loss 

through open water is up to 100 times greater than 

heat loss through thick sea ice. As a consequence, 

leads and polynyas (linear and nonlinear openings 

in the sea ice, respectively) are significant to the 

energy budget of the ice-covered ocean and to local 

and regional climatology. Such open-water areas and 

areas of reduced ice concentration are also important 

for shipping in ice-covered oceans.

 In addition to the importance of snow and ice maps 

in the GCMs, well-validated snow and ice maps are 

necessary for flood and drought (water quantity) prediction. And sea ice maps are needed for navigation 

and for energy-balance studies. MODIS snow and 

ice products can contribute to these efforts.

 In many regions, particularly in mountainous areas such as the western United States, knowledge of 

the amount of snow cover is critical in successfully 

predicting water supply, planning for agricultural and 

industrial developments, anticipating flooding, forecasting crop yield, and estimating freeze damage.

Product Overview

Global snow extent has been mapped by MODIS 

since shortly after the launch of the Terra satellite. 

The MODIS snow cover products are provided daily 

and as 8-day composites at 500-m resolution over the 

Earthʼs land surfaces, using an algorithm based on the 

normalized difference of a visible and a shortwaveinfrared band. In addition, daily and 8-day composite 

climate-modeling grid (CMG) maps are provided at 

0.05 ̊ resolution. The sea ice products derived from 

MODIS include sea ice extent and temperature, both 

during the day and at night. Products are provided at 

1-km resolution. The MODIS sea ice product is based 

on the normalized difference between a visible and a 

shortwave-infrared band. Global sea ice extent and 

ice-surface temperature (IST) are also mapped daily 

and as 8-day composite maps. The algorithm to map 

sea ice is similar to that used to map snow cover, 

but in addition, a split-window technique using two 

thermal bands calculates IST.

Product Interdependency and 

Continuity with Heritage Data

There have been several different satellite instruments 

that have collected snow and ice data for many years. 

Snow and ice are among the variables included in 

NASAʼs Pathfinder Program, which is providing 

research-quality data sets on global change from 

past and current satellite instruments. The heritage 

of the MODIS instrument is strong. Data sets from 

MODIS are extending those from historical and current sensors and providing the continuity needed for 

long-term global climate-change studies. The first 

detailed data sets of snow cover and sea ice cover 

came from the NOAA GOES, VHRR, and AVHRR 

instruments on polar-orbiting platforms. The Landsat Thematic Mapper (TM) was useful in developing 
Snow and Ice Cover • Data Products Handbook - Volume 1 195

The warmth of spring is reaching the northernmost waters of Baffin Bay between Greenland (right) and Canadaʼs Ellesmere Islands 

(left). This MODIS image from May 1, 2002 shows that the area of relatively open water is approaching Nares Strait (center), the last 

narrow passage way that connects the Atlantic Ocean to the Arctic. Image credit: Jacques Descloitres, MODIS Land Rapid Response 

Team, NASA/Goddard Space Flight Center. On the internet, visit visibleearth.nasa.gov to view a variety of MODIS data images.

Overview

the snow/cloud discrimination ability of the present 

MODIS snow and sea ice algorithms.

Suggested Reading

Ackerman, S. A. et al., 1998.

Armstrong, R. L., and M. J. Brodzik, 2002.

Carroll, T. R., 1995.

Cavalieri, D. J. et al., 2002.

Chang, A. T. C. et al., 1997.

Chang, A. T. C. et al., 2002.

Foster, J. L. et al., 1984.

Hall, D. K. et al., 1995.

Hall, D. K. et al., 2002a,b.

Key, J. R., and J. B. Collins, 1997. 

Klein, A. G. et al., 1998.

Klein, A. G., and J. Stroeve, 2002.

Magnuson, J. J. et al., 2000.

Parkinson, C. L., 2002.

Ramsay, B., 1998.

Riggs, G. A. et al., 1999.

Riggs, G. A. et al., 2002a,b.

Riggs, G. A., and D. K. Hall, 2002.
196 Data Products Handbook - Volume 1 • Snow and Ice Cover 

Daily MODIS Climate Modeling Grid (CMG) map at 0.05° resolution. Fractional snow 

cover in each pixel is also available. The darker gray over land represents cloud cover, 

lighter gray depicts snow-free areas, and white represents snow cover. On the internet, 

visit visibleearth.nasa.gov to view a variety of MODIS data images.

(MOD09). For surfaces determined to be non-forested 

by the MODIS land-cover product (MOD12), angular 

reflection effects are considered using a snow bi-directional reflectance function (BRDF) calculated with 

a discrete-ordinate radiative transfer model (Disort) 

and snowpack optical properties. Validation of the 

snow albedo began in 2002-2003 winter.

 The MODIS cloud mask (MOD35) (Ackerman 

et al., 1998) is used to mask clouds on the snow 

products. Two different versions of the cloud mask 

are used. A more liberal cloud mask is used for the 

swath-level products (MOD10_L2) and a conservative cloud mask is used for the tile (MOD10_A1 and 

MOD10_A2) and climate-modeling grid (CMG) 

(MOD10_C1 and MOD10_C2) products (Riggs 

and Hall).

Research and Applications

Large, inland water bodies such as the Great Lakes, 

are often ice covered during the winter months, and 

navigation during part of the winter is a significant 

problem. NOAA data are currently used to map ice 

cover on the Great Lakes, but discrimination between 

snow/ice and cloud is a problem. Additionally, ice 

cover on lakes can be an important climatic indicator, 

as the dates of freeze-up and break-up are influenced 

by meteorological conditions. A trend toward earlier 

break-up, for example, could signify a warming 

as has been observed in the Northern Hemisphere 

over the last 150 years (e.g. Magnuson et al., 2000). 

Thus, it is important to measure ice conditions on 

large lakes over an extended period of time in order 

to detect trends as well as for operational uses over 

the short term.

 Terra / MODIS

MODIS Snow Cover (MOD10)

Product Description

Global snow cover (including ice cover on large, 

inland water bodies) is mapped daily and as 8-day 

composites over the Earthʼs land surfaces at 500-m 

resolution. A global, daily snow-cover map has been 

produced since late February 2000. Snow cover, with 

its high albedo, is a key parameter of the global energy balance, reflecting much of the incident solar 

radiation back to space. Snow cover of the Northern 

Hemisphere is currently mapped by NOAA on a daily 

basis using their Interactive Multisensor Snow and Ice 

Mapping System (IMS) product (Ramsay, 1998). Another NOAA operational product, from the National 

Hydrologic Remote Sensing Center (NOHRSC) provides maps of most of the United States and parts of 

southern Canada, on nearly a daily basis during the 

Northern Hemisphere snow season (Carroll, 1995). 

The MODIS snow maps have been compared with 

the IMS and NOHRSC products. Though none of 

the snow maps has been determined to be the “gold 

standard,” the maps are shown to be in good agreement under most circumstances (Hall et al., 2002). 

However the maps are of different spatial resolutions 

and are intended for different purposes.

 A daily 500-m snow albedo product is also produced as part of the MODIS snow cover product 

(MOD10) (Klein and Stroeve, 2002). Once a pixel 

has been identified as being clear using the MODIS 

cloud mask (MOD35) and as snow covered using 

the MODIS snow mapping algorithm, both spectral 

and broadband albedos are computed using MODIS atmospherically-corrected surface reflectances 
Snow and Ice Cover • Data Products Handbook - Volume 1 197

Data Set Evolution

The snow-mapping algorithm that is used to produce 

the MODIS snow maps has a considerable heritage. It 

is based on the normalized difference of a visible and 

a shortwave-infrared band. This technique has been 

used, since at least 1978, to map snow from aircraft, 

and later from satellites (Kyle et al., 1978; Bunting 

and dʼEntremont, 1982; Dozier, 1984). Since the mid1980s, it has been used to map snow using Landsat 

data on the drainage-basin scale. The algorithm has 

been enhanced by incorporating the normalized difference vegetation index (NDVI) (Tucker, 1979), to 

map the snow cover in denser forests than was possible with the original algorithm (Klein et al., 1998). 

Other threshold tests are employed as well (Riggs et 

al., 2002a).

 Global snow cover has also been mapped using passive-microwave data at a resolution of about 50 km. 

While these data allow snow mapping through cloud 

cover, passive-microwave data have not provided a 

resolution that is suitable for detailed snow mapping 

at the basin scale. With the launch of the Advanced 

Microwave Scanning Radiometer (AMSR) on the 

Aqua satellite, snow maps may now be produced at 

a resolution of up to ~15 km (Chang et al., 2002). 

The expected use of passive-microwave and MODIS 

snow-cover data, together, should yield information 

on snow extent and snow-water equivalent (Armstrong and Brodzik, 2002).

Suggested Reading

Carroll, T. R. 1995.

Chang, A. T. C. et al., 2002.

Foster, J. L., and A. T. C. Chang, 1993.

Hall, D. K. et al., 1995.

Hall, D. K. et al., 2002a,b.

Klein et al., 1998.

Klein A. G., and J. Stroeve, 2002.

Ramsay, B., 1998.

Riggs, G. A. et al., 2002a,b.

Riggs, G. A., and D. K. Hall, 2002.

Salomonson, V. V. et al., 1995.

Terra / MODIS

MODIS Snow Cover Summary

Coverage: Global, daytime

Products Available:

MOD10_L2 (swath 500-m resolution), 

MOD10_A1 (tile 500-m resolution, daily),

MOD10_A2 (tile 500-m resolution, 8-day 

composite), 

MOD10_C1 (CMG 0.05° resolution, daily), 

MOD10_C2 (CMG 0.05° resolution, 8-day 

composite)

Spatial/Temporal Characteristics: 500 m/

daily; 500 m/8-day composite; 0.05° daily 

and 8-day composites

Key Geophysical Parameters: Snow cover, 

snow albedo, lake ice cover

Processing Level: 2, 3 (mapped)

Product Type: Standard

Maximum File Size: 34 MB (MOD10_L2);

12 MB (MOD10A1); 12 MB (MOD10A2);

104 MB (MOD10C1); 105 MB (MOD10C2) 

File Frequency: 288/day (MOD10_L2);

333/day (MOD10A1), 333/8 day period 

(MOD10A2), 1/day (MOD10C1), 1/8 day 

period (MOD10C2)

Primary Data Format: HDF-EOS

Additional Product Information: 

modis-snow-ice.gsfc.nasa.gov

DAAC: National Snow and Ice Data Center, 

nsidc.org/daac/

Science Team Contact: D. Hall
198 Data Products Handbook - Volume 1 Terra / MODIS • Snow and Ice Cover 

microwave sensors can map sea ice type (e.g., first 

year versus multiyear) (Zwally et al., 1983) and sea 

ice concentration (Parkinson, 2002).

 One advantage of using MODIS for mapping sea 

ice is its relatively high spatial resolution, and the 

fact that snow/cloud discrimination is possible using the normalized difference snow index (NDSI). 

Although there is still some ice/cloud confusion, 

it is possible to map sea ice cover and ice-surface 

temperature (IST) at a resolution of 1 km (Riggs et 

al., 1999). Thus the visible/near-IR and IR sensors 

such as MODIS and AVHRR are complementary to 

the passive-microwave sensors such as the AMSR, 

and the MODIS maps, when not precluded by clouds, 

provide a higher-resolution view of sea ice than is 

obtainable using passive-microwave data.

Data Set Evolution

The sea-ice mapping algorithm has a considerable 

heritage. It is based on the NDSI and has been shown 

to be effective for mapping sea ice as well as snow 

cover (Riggs et al., 1999 and 2002b). Additionally, 

IST is derived using a split-window technique based 

on the work of Key and Collins (1997) which has 

been used to map IST with AVHRR data.

Sea ice extent and ice-surface temperature (IST) on 

January 20, 2001, off the coast of Antarctica near 

the Napier Mountains. The IST product is 1-km 

resolution.

Cloud

Land

Cloud

Land

Cloud cover

261 K

to

271.5 K

> 271.5 K

Land

MODIS Sea Ice Cover and Ice 

Surface Temperature (MOD29)

Product Description

Global sea ice cover has been mapped daily and as 

8-day composites at 1-km resolution since shortly 

after the December 1999 launch of the Terra satellite. 

The MODIS sea ice cover products are based on an 

algorithm that utilizes the normalized difference snow 

index (NDSI). In addition, daily and 8-day composite 

climate-modeling grid (CMG) maps are produced at 

0.05o resolution. MODIS sea ice cover maps will 

complement passive-microwave-derived maps and 

maps produced using imaging radar. MODIS-derived 

ice-surface temperature (IST) maps at 1-km resolution, complement IST maps derived from the AMSR 

instrument, at 25-km resolution. 

Research and Applications

Sea ice cover is currently mapped by NOAA visible 

and near-infrared sensors, and by microwave sensors, 

both passive and active. Snow/cloud discrimination 

is a major hindrance in mapping sea ice using visible 

and near-infrared sensors, while microwave sensors, 

such as AMSR-E on the Aqua satellite (Cavalieri et 

al., in press), can map sea ice through cloud cover, 

but at a relatively low resolution. However, passive-
Snow and Ice Cover • Data Products Handbook - Volume 1 199

Suggested Reading

Cavalieri, D. J. et al., 2002.

Hall, D. K. et al., 1995.

Kyle, H. L. et al., 1978.

Magnuson, J. J. et al., 2000.

Parkinson, C. L., 1997.

Ramsay, B., 1998.

Terra / MODIS

MODIS Sea Ice Cover and Ice Surface 

Temperature Summary

Coverage: Global, daytime and nighttime 

over nonequatorial ocean

Products Available: MOD29 (sea ice extent 

and IST, 1-km resolution); MOD29P1D (sea 

ice extent and IST, 1-km resolution, daily 

daytime); MOD29P1N (IST 1-km resolution, 

daily nighttime)

Spatial/Temporal Characteristics: 1 km/

daily; 1 km 8-day composite; 0.05° daily 

and 8-day composite

Key Geophysical Parameters: Sea ice 

cover, sea ice surface temperature

Processing Level: 2, 3 (mapped)

Product Type: Standard

Maximum File Size:

MOD29: 20 MB

MOD29P1D: 6 MB

MOD29P1N: 4 MB

MOD29P2D and MOD29P2N: 12 MB

(8 day Level 3)

File Frequency: 

MOD29: 288/day; 

MOD29P1D, MOD29P1N, MOD29P2D 

and MOD29P2N: 378/day

Primary Data Format: HDF-EOS

Browse Available: No

Additional Product Information: 

modis-snow-ice.gsfc.nasa.gov

DAAC: National Snow and Ice Data Center, 

nsidc.org/daac/

Science Team Contact: D. Hall
201

Chapter 13

Data Assimilation 

System
202 Data Products Handbook - Volume 1 • Data Assimilation System 

Data Assimilation Office/Data Assimilation System 

– An Overview

 Overview

The Data Assimilation Office (DAO) develops research-quality 4-dimensional atmosphere-ocean-land 

data assimilation products for EOS. The data assimilation combines all available data into a dynamically 

consistent depiction of the Earth-atmosphere system, 

and has the unique ability to produce estimates of 

unobserved quantities.

 Data assimilation approaches are used throughout 

science and engineering. Most simply, data assimilation is the use of a mathematical-physical model to 

analyze observations, while at the same time quantifying the shortcomings of the model formulation.

 In Earth science, a data assimilation system is 

effectively an Earth-system model constrained by 

Earth-system observations. The data assimilation 

system: 1) organizes the observations from many 

diverse instrument sources and measurement times 

into a single useful data product; 2) complements 

the observations by propagation of information 

from observed into unobserved regions and times; 

3) provides estimates of expected values of the observations with which to assess data and instrument 

quality; 4) provides superior products for environmental assessment studies; and 5) supplements the 

observations by providing estimates of quantities that 

are difficult or impossible to observe.

 The Goddard EOS Data Assimilation System 

(GEOS DAS) provides Level 4 products of landsurface and atmospheric quantities. GEOS DAS 

operates on a 6-hour cycle; data are gathered over a 

6-hour window, and combined with a General Circulation Model (GCM) background estimate using an 

objective analysis system at a time in the middle of 

the 6-hour window. The output from the analysis is 

then used in the GCM to generate a forecast to provide 

the background fields in the middle of the next 6-hour 

assimilation window. The data flow into and out of 

the DAS is summarized in the Operational Data Flow 

Diagram, presented in this section.

 The GCM used in GEOS DAS uses a physical 

space discretization with a horizontal resolution of 

1° in latitude and 1.25° longitude. The GCM has a 

vertical discretization using approximately 55 layers 

with computational top near 0.01 hPa; this combiThe above image displays variables from the D4FAXMIS/D4LAXMIS and D4FAPMIS/D4LAPMIS products. The 

ability to determine the location and topography of the tropopause is of interest for a number of applications, including 

stratospheric chemistry. Top panels: sea level pressure (contours in 4 hPa intervals), jet stream wind speed (60 m/s), and 

tropopause potential temperature (shaded according to legend). Lower panels: three-dimensional perspective from a 

southern viewpoint of the above mentioned variables and the dynamic tropopause (1.5PVU surface) with tropopause 

potential temperature shaded according to legend (slp, uwnd, vwnd, tmpu).

452.70

396.63

340.56

284.49

228.42
Data Assimilation System • Overview Data Products Handbook - Volume 1 203

nation allows the GCM to have 

a fully-resolved stratosphere.

 The objective analysis used 

in GEOS DAS is the Physical-space Statistical Analysis 

System (PSAS). This is a 

full 3-dimensional variational 

analysis system which seeks 

to produce an optimal combination of disparate data with 

a 6-h GCM forecast estimate 

such that the resulting analysis 

errors are minimized.

Support for EOS 

Science Goals

The goals of the DAO during the EOS timeframe are to 

produce assimilation analyses 

to ensure that the maximum 

information is gained from 

EOS and other observations, 

and to set the foundation for 

future Earth system models. 

Continual research into numerical and theoretical techniques and associated Earth science process studies 

ensures that the developed system meets the needs of 

EOS. Analyzing historical and new data sets available 

in the pre-launch phase of EOS serves as the basis to 

confront Earth system problems, and the successes 

and failures of these diagnostic and interpretive 

studies will define the evolution of the data assimilation system. The assimilated data produced in the 

pre-launch period were made available to the broad 

science community to assure infusion of relevant 

diverse applications into the development process. 

A commitment is made to continued reanalysis of 

these data sets to minimize the impact of algorithm 

changes on interannual signals.

Operational Product Generation

DAO products are produced in two modes, a nearreal-time mode called “first-look” and a delayed 

mode called “late-look.” Both modes of operation 

use the same GEOS-DAS algorithm, but late-look 

uses higher quality observational inputs. Typically, a 

Upper Air Winds

Rawinsonde

Aircraft

Satellite Cloudtrack Winds

Surface Pressure

Land

Ships & Buoys

Boundary Conditions

NCEP SST

NCEP Sea Ice

Upper Air Temperature 

& Moisture

Rawinsonde

L1B TOVS Radiances

SSM/I Total Precipitable Water

Surface Winds

Ships & Buoys

QuikScat Scatterometer

DAO

Data 

Assimilation

System

Products:

D4LAXMIS, D4FAXMIS

D4LAPMIS, D4FAPMIS

D4LAXENG, D4FAXENG

D4LAXLSM, D4FAXLSM

D4LAXCLD, D4FAXCLD

D4LAPCLD

D4LAPMOM

D4LAPMST

D4LAPTMP

DAO Offline 

Ozone Assimilation

System

Ozone

SBUV Ozone Profiles

SBUV Total Ozone

Goddard DAAC

Products:

DLLAXCHM,DFLAXCHM

DLLAPCHM,DFLAPCHM

Surface Skin

Temperature

L1B TOVS Radiances

larger number of observations are available for latelook, and there is less chance of missing data due to 

problems with networks or data providers. Due to 

the accelerated nature of first-look processing, only 

a subset of DAO products is produced. First-look 

product IDs begin with “D4F” and late-look product 

IDs begin with “D4L”.

 Each DAO product is packaged in an HDF-EOS 

formatted data set spanning a single day. Some products are time-averaged, and others are instantaneous. 

This is discussed in the product description section 

of this chapter and in the File Specification documentation found at dao.gsfc.nasa.gov/Operations/. 

 Data products are transferred from DAO systems to 

the Goddard DAAC, from where they are distributed 

to subscribed customers. The nominal delivery time 

at the DAAC for first-look products valid for a given 

day is 11:00 GMT of the following day. The ozone 

products (D4FAXCHM, D4LAPCHM) are exceptions as they are delivered around 22:00 GMT of the 

following day. Late-look products are delivered to 

the DAAC with a 14-to-21 day delay.
204 Data Products Handbook - Volume 1 Data Assimilation System 

GEOS-3 Products

The discussion in this document refers to GEOS-4 

data. Version 4 of the GEOS-DAS became operational on October 1, 2002. Prior to this date, the DAO 

generated GEOS-3 first-look and late-look products 

that are stored at the GES DAAC under a different 

set of Earth Science Data Types (EDSTs). The products are very similar to GEOS-4, but there are some 

differences. For more information, see the GEOS-3 

File Specification on the DAO Operations web page 

(dao.gsfc.nasa.gov/Operations). Note that the period 

from January 1, 2000 through October 1, 2002 will 

be re-processed using GEOS-4.
Data Assimilation System Data Products Handbook - Volume 1 205

Suggested Reading

DAO ATBD, available on the DAO home page, 

dao.gsfc.nasa.gov

File Specification for GEOS-DAS Gridded Output, 

available at dao.gsfc.nasa.gov/Operations/

DAO-DAS Time-Averaged Single Level 

Cloud Quantities Product Summary

Coverage: Global

Spatial/Temporal Characteristics: 1.25° ×

1° lon-lat grid (288 × 181 grid points), 8 

times/file: 01.30, 04.30, 07.30, 10.30, 13.30, 

16.30, 19.30, and 22.30 UTC; three-hour 

average centered on timestamp

Key Geophysical Parameters: Clouds, radiation, optical depth

Processing Level: 4

Product Type: Standard, post-launch, latelook and first-look

Approximate File Size: 15 MB

File Frequency: 1/day

Primary Data Format: HDF-EOS

Additional Product Information: 

dao.gsfc.nasa.gov/Operations/

Science Team Contact: R. Atlas

DAO-DAS Time-Averaged 

Single Level Cloud Quantities

(D4FAXCLD, D4LAXCLD)

Product Description

This Level 4 product consists of a collection of 

time-averaged cloudiness and other cloud-related 

quantities produced by the DAO Data Assimilation 

System (DAS). These are typically saved every three 

hours, averaged for the three hours previous to the 

time stamp. This includes the total and clear sky 

outgoing long-and shortwave radiation, total cloud 

fraction, the low, middle, and high cloud fraction and 

optical depth, and cloud top temperature and pressure. 

Products are produced in both a near-real-time “first 

look” mode and a delayed or “late look” mode, which 

is described in the overview section of this chapter.

Research and Applications

The cloudiness fields are useful for a wide range of 

weather and climate studies.

Data Set Evolution

The data are the output of an assimilation process 

that combines various observations with a short-term 

forecast (or first guess) provided by the assimilation 

model. Details of the assimilation process for the 

current version of the DAO-DAS can be found in 

the DAO ATBD, located at dao.gsfc.nasa.gov. It 

is important to know that the quality of each DAS 

product depends on a number of factors including 

the quality and density of the relevant input observations, the ability of the forecast model to provide 

a reasonable first guess for the quantity of interest, 

and the degree to which the observations constrain 

that quantity.

 It is advisable to refer to the DAO-DAS data flow 

diagram for the input observations relevant to each 

of the quantities in this collection of DAS products. 

All quantities in this collection are diagnostic quantities generated by the model that should be used 

with caution. Clouds are not directly constrained by 

observations in the current DAO-DAS, and may be 

substantially impacted by model bias.

 The operational status of first-look and late-look 

product generation can be found on the DAO web 

page: dao.gsfc.nasa.gov/Operations/
206 Data Products Handbook - Volume 1 Data Assimilation System 

of the quantities in this collection of DAS products. 

This collection includes a number of diagnostic 

quantities generated by the model that should be 

used with caution. These fields, which include the 

precipitation, surface fluxes, and albedo, are not 

directly constrained by observations in the current 

DAO-DAS, and may be substantially impacted by 

model bias. Over land, the ground temperature, and 

the two-meter temperature and moisture are also only 

weakly constrained by the observations since the current version of the DAO-DAS does not assimilate 

surface/near surface temperature and moisture observations (see above for information about the ground 

temperature over the oceans).

 The operational status of first-look and late-look 

product generation can be found on the DAO web 

page: dao.gsfc.nasa.gov/Operations/

Suggested Reading

DAO ATBD, available on the DAO home page, 

dao.gsfc.nasa.gov

File Specification for GEOS-DAS Gridded Output, 

available at dao.gsfc.nasa.gov/Operations/

DAO-DAS Time-Averaged Near Surface 

and Vertically-Integrated Quantities 

Product Summary

Coverage: Global

Spatial/Temporal Characteristics: 1.25° ×

1° lon-lat grid (288 × 181 grid points), 8 

times/file: 01.30, 04.30, 07.30, 10.30, 13.30, 

16.30, 19.30, and 22.30 UTC; three-hour 

average centered on timestamp

Key Geophysical Parameters: Precipitation, 

temperature, moisture, albedo, radiation

Processing Level: 4

Product Type: Standard, post-launch, latelook and first-look

Approximate File Size: 52 MB

File Frequency: 1/day

Primary Data Format: HDF-EOS

Additional Product Information: 

dao.gsfc.nasa.gov/Operations/

Science Team Contact: R. Atlas

DAO-DAS Time-Averaged 

Near Surface and VerticallyIntegrated Quantities 

(D4FAXENG, D4LAXENG)

Product Description

This Level 4 product consists of a collection of timeaveraged near surface, vertically-integrated, and other 

single-level atmospheric and land quantities produced 

by the DAO Data Assimilation System (DAS). These 

are saved every three hours, averaged for the three 

hours centered on the time stamp. This includes the 

total precipitation, convective precipitation, surface 

evaporation, surface sensible heat flux, the ground 

(skin) temperature, the temperature and specific humidity at 2 and 10 meters, net radiation at the ground, 

albedo, and vertically-integrated moisture and heat 

fluxes, specific humidity, and analysis increments. 

Products are produced in both a near-real time “first 

look” mode and a delayed or “late look” mode, which 

is described in the overview section of this chapter. 

Note that over the ocean the ground temperature is 

the NCEP sea surface temperature (SST) product 

specified as a boundary condition in the DAS.

Research and Applications

The surface/near surface fields provide valuable 

information for a wide variety of land and ocean, 

and weather and climate studies. The vertically-averaged fields are convenient for studies of various 

atmospheric budgets and other diagnostic calculations. The vertically-integrated analysis increments 

are included to close the budget equations.

Data Set Evolution

The data are the output of an assimilation process 

that combines various observations with a short term 

forecast (or first guess) provided by the assimilation 

model. Details of the assimilation process for the 

current version of the DAO-DAS can be found in 

the DAO ATBD, located at dao.gsfc.nasa.gov. It 

is important to know that the quality of each DAS 

product depends on a number of factors including 

the quality and density of the relevant input observations, the ability of the forecast model to provide 

a reasonable first guess for the quantity of interest, 

and the degree to which the observations constrain 

that quantity.

 It is advisable to check the DAO-DAS data flow 

diagram for the input observations relevant to each 
Data Assimilation System Data Products Handbook - Volume 1 207

Suggested Reading

DAO ATBD, available on the DAO home page, 

dao.gsfc.nasa.gov

File Specification for GEOS-DAS Gridded Output, 

available at dao.gsfc.nasa.gov/Operations/

DAO-DAS Time-Averaged 2-Dimensional 

Surface Data Product Summary

Coverage: Global

Spatial/Temporal Characteristics: 1.25° ×

1° lon-lat grid (288 × 181 grid points), 8 

times/file: 01.30, 04.30, 07.30, 10.30, 13.30, 

16.30, 19.30, and 22.30 UTC; three-hour 

average centered on timestamp

Key Geophysical Parameters: Precipitation, 

radiation, temperature, vegetation

Processing Level: 4

Product Type: Standard, post-launch, latelook and first-look

Approximate File Size: 12 MB

File Frequency: 1/day

Primary Data Format: HDF-EOS

Additional Product Information: 

dao.gsfc.nasa.gov/Operations/

Science Team Contact: R. Atlas

DAO-DAS Time-Averaged

2-Dimensional Surface Data 

(D4FAXLSM, D4LAXLSM)

Product Description

This Level 4 product consists of 2-dimensional timeaveraged surface data produced by the DAO Data 

Assimilation System (DAS). These are saved every 

three hours, averaged for the three hours centered 

on the time stamp. This includes the components of 

the total precipitation, components of the longwave 

radiation, components of the photosynthetically active radiation, snow cover, vegetation characteristics 

and diagnostics of the temperature prediction. The 

product is gridded, and has one level equivalent to 

the model surface. Products are produced in both a 

near-real-time “first look” mode and a delayed or 

“late look” mode, which is described in the overview 

section of this chapter.

Research and Applications

The product has 3-hourly intervals to represent the 

diurnal cycle of the precipitation and energy budget 

terms. The precipitation is divided into convective, 

large-scale and frozen components. Both upwelling 

and downwelling longwave radiation fluxes at the 

model surface are provided. Leaf area index and 

greenness are generally prescribed quantities.

Data Set Evolution

In general, the variables in this product are based on 

model quantities. However, the user should confirm 

which data are being assimilated for any given data 

set. For example, precipitation and land data assimilation techniques are under development, and may 

be implemented, if the appropriate data are available. 

Likewise, the source of data to prescribe leaf area index and greenness should be confirmed. Furthermore, 

the parameterization of the land surface is actively 

developing, and may be updated or changed more 

frequently than this document. The DAO provides 

comprehensive descriptions of system updates as 

they are made.

 The operational status of first-look and late-look 

product generation can be found on the DAO web 

page: dao.gsfc.nasa.gov/Operations/
208 Data Products Handbook - Volume 1 Data Assimilation System 

used with caution since they are sensitive to errors 

in the modelʼs gravity wave drag parameterization. 

Similarly, quantities such as the surface drag coefficient, surface roughness, and PBL height are model 

diagnostic fields that are not directly constrained by 

the observations.

Suggested Reading

Atlas, R. et al., in press.

DAO ATBD, available on the DAO home page, 

dao.gsfc.nasa.gov

File Specification for GEOS-DAS Gridded Output, 

available at dao.gsfc.nasa.gov/Operations/

DAO-DAS Time-Averaged Surface and 

Top-of-the-Atmosphere Stresses 

Product Summary

Coverage: Global

Spatial/Temporal Characteristics: 1.25° × 1° 

lon-lat grid (288 × 181 grid points), 8 times/

file: 01.30, 04.30, 07.30, 10.30, 13.30, 16.30, 

19.30, and 22.30 UTC; three-hour average 

centered on timestamp

Key Geophysical Parameters: Pressure, 

surface stress

Processing Level: 4

Product Type: Standard, post-launch, latelook and first-look

Approximate File Size: 22 MB

File Frequency: 1/day

Primary Data Format: HDF-EOS

Additional Product Information: 

dao.gsfc.nasa.gov/Operations/

Science Team Contact: R. Atlas

DAO-DAS Time-Averaged 

Surface and Top-of-theAtmosphere Stresses 

(D4FAXSTR, D4LAXSTR)

Product Description

This Level 4 product consists of time-averaged surface frictional stresses and surface and top of the atmosphere stresses due to gravity-wave drag produced 

by the DAO Data Assimilation System (DAS). These 

are typically saved every three hours, averaged for the 

three hours centered on the time stamp. Also included 

in this collection of quantities are the surface drag 

coefficient, friction velocity, surface roughness, and 

the estimated height of the planetary boundary layer 

(PBL). Products are produced in both a near-realtime “first look” mode and a delayed or “late look” 

mode, which is described in the overview section of 

this chapter.

Research and Applications

The surface stress fields are useful for a wide variety 

of weather and climate studies, particularly those focused on atmosphere-ocean interaction.

Data Set Evolution

The data are the output of an assimilation process 

that combines various observations with a short term 

forecast (or first guess) provided by the assimilation 

model. Details of the assimilation process for the 

current version of the DAO-DAS can be found in 

the DAO ATBD, located at dao.gsfc.nasa.gov. It 

is important to know that the quality of each DAS 

product depends on a number of factors, including 

the quality and density of the relevant input observations, the ability of the forecast model to provide 

a reasonable first guess for the quantity of interest, 

and the degree to which the observations constrain 

that quantity.

 It is advisable to refer to the DAO-DAS data flow 

diagram for the input observations relevant for each of 

the quantities in this collection of DAS products. The 

surface stress fields in this collection are strongly tied 

to the analyzed surface winds that, over the oceans, 

are well constrained by various surface wind observations (e.g., SSM/I winds speed, QuikScat etc., see 

Atlas et al.). The gravity wave stresses should be 
Data Assimilation System Data Products Handbook - Volume 1 209

Suggested Reading

DAO ATBD, available on the DAO home page, 

dao.gsfc.nasa.gov

File Specification for GEOS-DAS Gridded Output, 

available at dao.gsfc.nasa.gov/Operations/

DAO-DAS Time-Averaged 3-Dimensional 

Cloud Quantities Product Summary

Coverage: Global

Spatial/Temporal Characteristics: 1.25° × 1° 

lon-lat grid, 36 pressure levels in the vertical 

(360 × 181 × 36 grid points), 4 times/file: 03, 

09, 15, and 21 UTC; data are 6-hour time averages centered at the timestamp

Key Geophysical Parameters: Clouds, radiation

Processing Level: 4

Product Type: Standard, post-launch, late-look

Approximate File Size: 90 MB

File Frequency: 1/day

Primary Data Format: HDF-EOS

Additional Product Information: 

dao.gsfc.nasa.gov/Operations/

Science Team Contact: R. Atlas

DAO-DAS Time-Averaged

3-Dimensional Cloud 

Quantities

(D4LAPCLD)

Product Description

This Level 4 product consists of a collection of timeaveraged 3-dimensional cloud-related quantities produced by the DAO Data Assimilation System (DAS). 

These are typically saved every six hours, averaged 

for the three hours previous to and the three hours following the time stamp. This includes the cloud optical 

depth, the total cloud fraction, and the convective 

cloud fraction. These products are produced only in a 

delayed or “late look” mode, which is described in the 

overview section of this chapter. The fields are gridded and provided on pressure levels as documented 

in the GEOS-DAS File Specification.

Research and Applications

The cloudiness fields are useful for a wide range of 

weather and climate studies.

Data Set Evolution

The data are the output of an assimilation process 

that combines various observations with a short-term 

forecast (or first guess) provided by the assimilation 

model. Details of the assimilation process for the 

current version of the DAO-DAS can be found in 

the DAO ATBD, located at dao.gsfc.nasa.gov. It 

is important to know that the quality of each DAS 

product depends on a number of factors including 

the quality and density of the relevant input observations, the ability of the forecast model to provide 

a reasonable first guess for the quantity of interest, 

and the degree to which the observations constrain 

that quantity.

 It is advisable to refer to the DAO-DAS data flow 

diagram for the input observations relevant for each 

of the quantities in this collection of DAS products. 

All quantities in this collection are diagnostic quantities generated by the model that should be used 

with caution. Clouds are not directly constrained by 

observations in the current DAO-DAS, and may be 

substantially impacted by model bias.

 The operational status of first-look and late-look 

product generation can be found on the DAO web 

page: dao.gsfc.nasa.gov/Operations/
210 Data Products Handbook - Volume 1 Data Assimilation System 

Suggested Reading

DAO ATBD, available on the DAO home page, 

dao.gsfc.nasa.gov

File Specification for GEOS-DAS Gridded Output, 

available at dao.gsfc.nasa.gov/Operations/

DAO-DAS Time-Averaged 3-Dimensional 

Wind Tendency Fields Product Summary

Coverage: Global

Spatial/Temporal Characteristics: 1.25° × 1° 

lon-lat grid, 36 pressure levels in the vertical 

(360 × 181 × 36 grid points), 4 times/file: 03, 

09, 15, and 21 UTC; data are 6-hour time 

averages centered at the timestamp

Key Geophysical Parameters: Wind tendencies

Processing Level: 4

Product Type: Standard, post-launch, late-look

Approximate File Size: 150 MB

File Frequency: 1/day

Primary Data Format: HDF-EOS

Additional Product Information: 

dao.gsfc.nasa.gov/Operations/

Science Team Contact: R. Atlas

DAO-DAS Time-Averaged

3-Dimensional Wind Tendency 

Fields (D4LAPMOM)

Product Description

This Level 4 product consists of a collection of timeaveraged 3-dimensional wind forcing fields produced 

by the DAO Data Assimilation System (DAS). These 

are typically saved every six hours, averaged for the 

three hours previous to and the three hours following 

the time stamp. This includes the wind forcing due to 

turbulence, gravity-wave drag, Rayleigh friction, and 

the analysis increments. These products are produced 

only in a delayed or “late look” mode, which is described in the overview section of this chapter. The 

fields are gridded and provided on pressure levels as 

documented in the GEOS-DAS File Specification.

Research and Applications

The wind forcing fields are useful for a wide range 

of weather and climate budget and diagnostic studies. The analysis increments are included to close the 

momentum budgets.

Data Set Evolution

The data are the output of an assimilation process 

that combines various observations with a short-term 

forecast (or first guess) provided by the assimilation 

model. Details of the assimilation process for the 

current version of the DAO-DAS can be found in 

the DAO ATBD, located at dao.gsfc.nasa.gov. It 

is important to know that the quality of each DAS 

product depends on a number of factors including 

the quality and density of the relevant input observations, the ability of the forecast model to provide 

a reasonable first guess for the quantity of interest, 

and the degree to which the observations constrain 

that quantity.

 It is advisable to refer to the DAO-DAS data flow 

diagram for the input observations relevant for each 

of the quantities in this collection of DAS products. 

All quantities in this collection are diagnostic quantities generated by the model that should be used with 

caution. The forcing fields are produced by the model 

parameterizations and are not directly constrained by 

observations in the current DAO-DAS. They may be 

substantially impacted by model bias.

 The operational status of first-look and late-look 

product generation can be found on the DAO web 

page: dao.gsfc.nasa.gov/Operations/
Data Assimilation System Data Products Handbook - Volume 1 211

Suggested Reading

DAO ATBD, available on the DAO home page, 

dao.gsfc.nasa.gov

File Specification for GEOS-DAS Gridded Output, 

available at dao.gsfc.nasa.gov/Operations/

DAO-DAS Time-Averaged 3-Dimensional 

Moisture Tendency Fields Product 

Summary

Coverage: Global

Spatial/Temporal Characteristics: 1.25° × 1° 

lon-lat grid, 36 pressure levels in the vertical 

(360 × 181 × 36 grid points), 4 times/file: 03, 

09, 15, and 21 UTC; data are 6-hour time averages centered at the timestamp

Key Geophysical Parameters: Transport parameters

Processing Level: 4

Product Type: Standard, post-launch, late-look

Approximate File Size: 60 MB

File Frequency: 1/day

Primary Data Format: HDF-EOS

Additional Product Information: 

dao.gsfc.nasa.gov/Operations/

Science Team Contact: R. Atlas

DAO-DAS Time-Averaged

3-Dimensional Moisture 

Tendency Fields (D4LAPMST)

Product Description

This Level 4 product consists of a collection of timeaveraged 3-dimensional moisture tendency fields produced by the DAO Data Assimilation System (DAS). 

These are typically saved every six hours, averaged 

for the three hours previous to and the three hours 

following the time stamp. This includes the moisture 

tendency due to turbulence, moist processes, and the 

analysis increments. These products are produced 

only in a delayed or “late look” mode, which is described in the overview section of this chapter. The 

fields are gridded and provided on pressure levels as 

documented in the GEOS-DAS File Specification.

Research and Applications

The moisture tendency fields are useful for a wide 

range of weather and climate budget and diagnostic 

studies. The analysis increments and moisture filling 

are included to close the moisture budget.

Data Set Evolution

The data are the output of an assimilation process 

that combines various observations with a short term 

forecast (or first guess) provided by the assimilation 

model. Details of the assimilation process for the 

current version of the DAO-DAS can be found in 

the DAO ATBD, located at dao.gsfc.nasa.gov. It 

is important to know that the quality of each DAS 

product depends on a number of factors including 

the quality and density of the relevant input observations, the ability of the forecast model to provide 

a reasonable first guess for the quantity of interest, 

and the degree to which the observations constrain 

that quantity.

 It is advisable to refer to the DAO-DAS data flow 

diagram for the input observations relevant for each of 

the quantities in this collection of DAS products. All 

quantities in this collection are diagnostic quantities 

generated by the model that should be used with caution. The tendency fields are produced by the model 

parameterizations and are not directly constrained by 

observations in the current DAO-DAS. They may be 

substantially impacted by model bias.

 The operational status of first-look and late-look 

product generation can be found on the DAO web 

page: dao.gsfc.nasa.gov/Operations/
212 Data Products Handbook - Volume 1 Data Assimilation System 

Suggested Reading

Stajner, I. et al., 2001.

DAO ATBD, available on the DAO home page, 

dao.gsfc.nasa.gov

File Specification for GEOS-DAS Gridded Output, 

available at dao.gsfc.nasa.gov/Operations/

DAO-DAS Total Column Ozone Product 

Summary

Coverage: Global

Spatial/Temporal Characteristics: 2.5° × 2.0° 

lon-lat grid, (144 × 91 grid points), 8 times/file: 

00, 03, 06, 09, 12, 15, 18 and 21 UTC; data 

are instantaneous, valid at the timestamp

Key Geophysical Parameters: Total ozone

Processing Level: 4

Product Type: Standard, post-launch, late-look 

and first-look

Approximate File Size: 0.5 MB

File Frequency: 1/day

Primary Data Format: HDF-EOS

Additional Product Information: 

dao.gsfc.nasa.gov/Operations/

Science Team Contact: R. Atlas

DAO-DAS Total Column 

Ozone (D4FAXCHM, 

D4LAXCHM)

Product Description

This Level 4 product contains instantaneous vertically 

integrated atmospheric ozone columns produced by 

the DAOʼs ozone data assimilation system (Stajner et 

al., 2001). It is produced in both near-real-time “first 

look” mode and in a delayed “late look” mode, which 

is described in the overview section of this chapter. 

The fields are two-dimensional and gridded.

Research and Applications

Global total column ozone distribution is needed in 

climate and weather studies. These global vertically 

integrated ozone columns are convenient for studies 

of ozone budget. Total column ozone is a key factor 

determining the amount of ultraviolet radiation reaching the Earthʼs surface. Total column ozone amounts 

are needed in retrievals of geophysical fields from 

satellite instrument measurements.

Data Set Evolution

Total column ozone and stratospheric ozone profile 

observations are assimilated into an off-line ozone 

transport model in repeating assimilation cycles. In 

one assimilation cycle, an ozone analysis from the 

previous assimilation cycle is advected to produce 

a short-term ozone forecast. The advection is done 

using an off-line transport (forecast) model driven by 

assimilated winds from the meteorological DAO data 

assimilation system. The time step of this transport 

model is 15 minutes, and the observations are introduced to the model after every transport time step. 

The ozone forecast and observations are combined 

in a statistical analysis according to specifications 

of their respective error statistics. A global physical 

space-based statistical analysis scheme is used. Another assimilation cycle is started from the resulting 

analyzed ozone field. The three-dimensional analyzed 

ozone field is vertically integrated to provide analyzed 

total column ozone every three hours.
Data Assimilation System Data Products Handbook - Volume 1 213

 It is advisable to refer to the DAO-DAS data flow 

diagram for the input observations relevant for each 

of the quantities in this collection of DAS products. 

In the current version of the DAO-DAS, the surface 

winds over the ocean are strongly constrained by wind 

estimates provided by a number satellites (SSM/I, 

NSCAT, QuikScat, see Atlas et al.). Total moisture is 

well constrained by SSM/I estimates of total precipitable water (see Hou et al.). The vertically-integrated 

temperature and winds are also well-constrained by 

both satellite and conventional observations. The near 

surface temperature, humidity, and winds over land 

should be used with caution since they are weakly 

constrained by observations and sensitive to model 

bias.

 The operational status of first-look and late-look 

product generation can be found on the DAO web 

page: dao.gsfc.nasa.gov/Operations/

Suggested Reading

Atlas, R. et al., in press.

Hou, A. et al., 2000.

DAO ATBD, available on the DAO home page, 

dao.gsfc.nasa.gov

File Specification for GEOS-DAS Gridded Output, 

available at dao.gsfc.nasa.gov/Operations/

DAO-DAS Instantaneous 

Near Surface and VerticallyIntegrated State Variables 

(D4FAXMIS, D4LAXMIS)

Product Description

This Level 4 product consists of a collection of instantaneous near surface, vertically-integrated, and other 

single-level atmospheric and land quantities produced 

by the DAO Data Assimilation System (DAS). This 

includes winds, temperature, and moisture at two and 

ten meters; ground temperature; vertically-averaged 

winds, temperature and moisture; sea level pressure; 

and tropopause temperature and pressure. Products 

are produced in both a near-real-time “first look” 

mode and a delayed or “late look” mode, which is 

described in the overview section of this chapter. 

Note that over the ocean the ground temperature 

is the NCEP sea surface temperature (SST) product 

specified as a boundary condition in the DAS. Other 

useful quantities included in this collection are the 

geopotential height and pressure at the Earthʼs surface at the resolution of the assimilating model, as 

well as the distribution of the surface types used by 

the model.

Research and Applications

The near surface fields provide valuable information 

for a wide variety of land and ocean, and weather 

and climate studies. The vertically-averaged fields 

are convenient for studies of various atmospheric 

budgets and other diagnostic calculations. The tropopause information is provided to aid in the investigation of tropospheric/stratospheric exchange and 

related studies.

Data Set Evolution

The data are the output of an assimilation process 

that combines various observations with a short-term 

forecast (or first guess) provided by the assimilation 

model. Details of the assimilation process for the 

current version of the DAO-DAS can be found in 

the DAO ATBD, located at dao.gsfc.nasa.gov. It 

is important to know that the quality of each DAS 

product depends on a number of factors including 

the quality and density of the relevant input observations, the ability of the forecast model to provide 

a reasonable first guess for the quantity of interest, 

and the degree to which the observations constrain 

that quantity.
214 Data Products Handbook - Volume 1 Data Assimilation System 

DAO-DAS Instantaneous Near Surface 

and Vertically-Integrated State Variables 

Product Summary

Coverage: Global

Spatial/Temporal Characteristics: 1.25° ×

1° lon-lat grid, (360 × 181), 8 times/file: 00, 

03, 06, 09, 12, 15, 18, and 21 UTC; data are 

instantaneous, valid at the timestamp

Key Geophysical Parameters: Sea-level and 

surface pressure, surface moisture, temperature and winds, tropopause temperature and 

pressure, and vertically averaged temperature 

and winds

Processing Level: 4

Product Type: Standard, post-launch, late-look 

and first-look

Approximate File Size: 32 MB

File Frequency: 1/day

Primary Data Format: HDF-EOS

Additional Product Information: 

dao.gsfc.nasa.gov/Operations/

Science Team Contact: R. Atlas
Data Assimilation System Data Products Handbook - Volume 1 215

space-based statistical analysis scheme is used. Another assimilation cycle is started from the resulting 

analyzed ozone field. The analyzed three-dimensional 

ozone field on model levels is interpolated to 36 constant pressure surfaces and saved every 6 hours.

 The operational status of first-look and late-look 

product generation can be found on the DAO web 

page: dao.gsfc.nasa.gov/Operations/

Suggested Reading

Stajner, I. et al., 2001.

DAO ATBD, available on the DAO home page, 

dao.gsfc.nasa.gov

File Specification for GEOS-DAS Gridded Output, 

available at dao.gsfc.nasa.gov/Operations/

DAO-DAS Ozone Mixing Ratio Product 

Summary

Coverage: Global

Spatial/Temporal Characteristics: 2.5° × 2.0° 

lon-lat grid, 36 pressure levels in the vertical 

(144 × 91 × 36 grid points), 4 times/file: 00, 06, 

12, and 18 UTC; data are instantaneous, valid 

at the timestamp

Key Geophysical Parameters: Ozone

Processing Level: 4

Product Type: Standard, post-launch, late-look 

and first-look

Approximate File Size: 9 MB

File Frequency: 1/day

Primary Data Format: HDF-EOS

Additional Product Information: 

dao.gsfc.nasa.gov/Operations/

Science Team Contact: R. Atlas

DAO-DAS Ozone Mixing Ratio 

(D4FAPCHM, D4LAPCHM)

Product Description

This Level 4 product contains instantaneous threedimensional ozone fields produced by the DAOʼs 

ozone data assimilation system. It is produced in 

both near-real-time “first look” mode and in a delayed 

“late look” mode, which is described in the overview 

section of this chapter. The fields are gridded and 

provided on pressure levels as documented in the 

GEOS-DAS File Specification.

 Note that the ozone mixing ratio fields at the following levels: 1000, 975, 950, 925, 900, and 875 hPa 

are provided at the request of users. No geophysical 

significance should be attributed to ozone distribution 

at these levels. The ozone values provided at these 

levels are extrapolated from the values at 850 hPa. 

Ozone data are assimilated between 850 and 0.2 

hPa.

Research and Applications

Global three-dimensional ozone distribution is 

needed in climate and weather studies, in determining the amount of ultraviolet radiation reaching 

the Earthʼs surface, and in retrievals of geophysical 

fields from satellite instrument measurements. The 

ozone profiles can be used as first guess fields in 

an under-determined problem of ozone retrievals 

from satellite instrument measurements. They can 

be used in validation or monitoring of independent 

ozone measurements. Ozone distribution is also 

needed in radiative transfer calculations in general 

circulation models.

Data Set Evolution

Total column ozone and stratospheric ozone profile 

observations are assimilated into an off-line ozone 

transport model in repeating assimilation cycles. In 

one assimilation cycle, an ozone analysis from the 

previous assimilation cycle is advected to produce 

short-term ozone forecast. The advection is done using an off-line transport (forecast) model driven by 

assimilated winds from the meteorological DAO data 

assimilation system. The time step of this transport 

model is 15 minutes, and the observations are introduced to the model after every transport time step. 

The ozone forecast and observations are combined 

in a statistical analysis according to specifications 

of their respective error statistics. A global physical 
216 Data Products Handbook - Volume 1 Data Assimilation System 

well constrained by observations (TOVS, SSM/I 

TPW, limited radiosonde), though values near the 

surface and in the upper troposphere and stratosphere 

must be used with caution (see Wu et al.). The vertical 

velocity is not observed. It is produced by the model, 

though it is of course consistent with the available 

other observations (horizontal winds, temperature, 

etc.). Turbulent kinetic energy is also a model-derived 

field: it is not directly constrained by observations. It 

should be considered an experimental product.

 The operational status of first-look and late-look 

product generation can be found on the DAO web 

page: dao.gsfc.nasa.gov/Operations/

Suggested Reading

Wu, M. L. et al., 2001.

DAO ATBD, available on the DAO home page, 

dao.gsfc.nasa.gov

File Specification for GEOS-DAS Gridded Output, 

available at dao.gsfc.nasa.gov/Operations/

DAO-DAS Instantaneous 3-Dimensional 

State Variables Product Summary

Coverage: Global

Spatial/Temporal Characteristics: 1.25° × 1° 

lon-lat grid, 36 pressure levels in the vertical 

(288 × 181 × 36 grid points), 4 times/file: 00, 

06, 12, and 18 UTC; data are instantaneous, 

valid at the timestamp

Key Geophysical Parameters: Winds, temperature, geopotential heights, moisture, and 

kenetic energy

Processing Level: 4

Product Type: Standard, post-launch, late-look 

and first-look

Approximate File Size: 180 MB

File Frequency: 1/day

Primary Data Format: HDF-EOS

Additional Product Information: 

dao.gsfc.nasa.gov/Operations/

Science Team Contact: R. Atlas

DAO-DAS Instantaneous

3-Dimensional State Variables 

(D4FAPMIS, D4LAPMIS)

Product Description

This Level 4 product consists of a collection of instantaneous 3-dimensional fields of atmospheric state and 

other atmospheric variables produced by the DAO 

Data Assimilation System (DAS). This includes the 

horizontal winds, temperature, moisture, geopotential 

height and vertical motion fields. Products are produced in both a near-real-time “first look” mode and a 

delayed or “late look” mode, which is described in the 

overview section of this chapter. The fields are gridded and provided on pressure levels as documented 

in the GEOS-DAS File Specification.

Research and Applications

The 3-dimensional atmospheric state and other fields 

included in this collection are critical for a wide variety of weather and climate studies. They provide 

basic information about the dynamics and thermodynamics of the atmosphere, including aspects of the 

atmospheric component of the hydrological cycle 

(e.g., atmospheric moisture content, atmospheric 

moisture flux).

Data Set Evolution

The data are the output of an assimilation process 

that combines various observations with a short-term 

forecast (or first guess) provided by the assimilation 

model. Details of the assimilation process for the 

current version of the DAO-DAS can be found in 

the DAO ATBD, located at dao.gsfc.nasa.gov. It 

is important to know that the quality of each DAS 

product depends on a number of factors including 

the quality and density of the relevant input observations, the ability of the forecast model to provide 

a reasonable first guess for the quantity of interest, 

and the degree to which the observations constrain 

that quantity.

 It is advisable to refer to the DAO-DAS data flow 

diagram for the input observations relevant for each 

of the quantities in this collection of DAS products. 

In the current version of the DAO-DAS, the winds, 

temperature, and geopotential height are well-constrained by a combination of in situ and satellite observations (radiosonde, cloud track winds, TOVS, 

SSM/I TPW, etc.). The moisture field is reasonably 
Data Assimilation System Data Products Handbook - Volume 1 217

 The operational status of first-look and late-look 

product generation can be found on the DAO web 

page: dao.gsfc.nasa.gov/Operations/

Suggested Reading

DAO ATBD, available on the DAO home page, 

dao.gsfc.nasa.gov

File Specification for GEOS-DAS Gridded Output, 

available at dao.gsfc.nasa.gov/Operations/

DAO-DAS Time-Averaged 3-Dimensional 

Temperature Tendency Fields Product 

Summary

Coverage: Global

Spatial/Temporal Characteristics: 1.25° × 1° 

lon-lat grid, 36 pressure levels in the vertical 

(288 × 181 × 36 grid points), 4 times/file: 03, 

09, 15, and 21 UTC; data are 6 hour time-averages centered at the timestamp

Key Geophysical Parameters: Temperature 

tendencies

Processing Level: 4

Product Type: Standard, post-launch, late-look

Approximate File Size: 150 MB

File Frequency: 1/day

Primary Data Format: HDF-EOS

Additional Product Information: 

dao.gsfc.nasa.gov/Operations/

Science Team Contact: R. Atlas

DAO-DAS Time-Averaged 

3-Dimensional Temperature 

Tendency Fields 

(D4LAPTMP)

Product Description

This level-4 product consists of a collection of timeaveraged 3-dimensional temperature tendency fields 

produced by the DAO Data Assimilation System 

(DAS). These are typically saved every six hours, 

averaged for the three hours previous to and the 

three hours following the time stamp. This includes 

the temperature tendency due to turbulence, moist 

processes, radiative heating/cooling, Rayleigh friction, gravity wave drag, and the analysis increments. 

These products are produced only in a delayed or “late 

look” mode, which is described in the overview. The 

fields are gridded and provided on pressure levels as 

documented in the GEOS-DAS File Specification.

Research and Applications

The temperature tendency fields are useful for a wide 

range of weather and climate budget and diagnostic 

studies. The analysis increments are included to close 

the moisture budget.

Data Set Evolution

The data are the output of an assimilation process 

that combines various observations with a short term 

forecast (or first guess) provided by the assimilation 

model. The basic data flow for the DAO DAS is illustrated in the overview. Details of the assimilation 

process for the current version of the DAO-DAS may 

be found in DAO ATBD. It is important to know 

that the quality of each DAS product depends on a 

number of factors including the quality and density 

of the relevant input observations, the ability of the 

forecast model to provide a reasonable first guess for 

the quantity of interest, and the degree to which the 

observations constrain that quantity.

 It is advisable to check the DAO-DAS data flow 

diagram to check on the input observations relevant 

for each of the quantities in this collection of DAS 

products. All quantities in this collection are diagnostic quantities generated by the model that should 

be used with caution. The tendency fields are produced by the model parameterizations and are not 

directly constrained by observations in the current 

DAO-DAS. They may be substantially impacted by 

model bias. 
218 Data Products Handbook - Volume 1 Data Assimilation System

Suggested Reading

DAO ATBD, available on the DAO home page, 

dao.gsfc.nasa.gov

File Specification for GEOS-DAS Gridded Output, 

available at dao.gsfc.nasa.gov/Operations/

DAO-DAS Time-Averaged 3-Dimensional 

Eddy-Diffusivity and Cloud Mass Flux 

Fields Product Summary

Coverage: Global

Spatial/Temporal Characteristics: 1.25° × 1° 

lon-lat grid, 36 pressure levels in the vertical 

(288 × 181 × 36 grid points), 4 times/file: 03, 

09, 15, and 21 UTC; data are 6 hour time-averages centered at the timestamp

Key Geophysical Parameters: Transport parameters

Processing Level: 4

Product Type: Standard, post-launch, late-look

Approximate File Size: 150 MB

File Frequency: 1/day

Primary Data Format: HDF-EOS

Additional Product Information: 

dao.gsfc.nasa.gov/Operations/

Science Team Contact: R. Atlas

DAO-DAS Time-Averaged 

3-Dimensional EddyDiffusivity and Cloud Mass 

Flux Fields

(D4LAPTRP)

Product Description

This level-4 product consists of time-averaged 3-

dimensional Eddy-Diffusivity and Cloud Mass Flux 

Fields produced by the DAO Data Assimilation System (DAS). These are typically saved every six hours, 

averaged for the three hours previous to and the three 

hours following the time stamp. This includes the 

eddy diffusivity for scalers and momentum, the cloud 

mass flux, and the detrainment cloud mass flux. These 

products are produced only in a delayed or “late look” 

mode, which is described in the overview.

Research and Applications

These fields are useful for various off-line transport 

calculations.

Data Set Evolution

The data are the output of an assimilation process 

that combines various observations with a short term 

forecast (or first guess) provided by the assimilation 

model. The basic data flow for the DAO DAS is illustrated in this chapterʼs overview. Details of the 

assimilation process for the current version of the 

DAO-DAS may be found in the DAO ATBD. It is important to know that the quality of each DAS product 

depends on a number of factors including the quality 

and density of the relevant input observations, the 

ability of the forecast model to provide a reasonable 

first guess for the quantity of interest, and the degree 

to which the observations constrain that quantity.

 It is advisable to refer to the DAO-DAS data flow 

diagram for the input observations relevant to each of 

the quantities in this collection of DAS products. All 

quantities in this collection are diagnostic quantities 

generated by the model that should be used with caution. The eddy diffusivity and mass flux fields are a 

product of the model parameterizations and are not 

directly constrained by observations in the current 

DAO-DAS. They may be substantially impacted by 

model bias. 

 The operational status of first-look and late-look 

product generation can be found on the DAO web 

page: dao.gsfc.nasa.gov/Operations/
Appendix A: EOS DAAC Contact Information Data Products Handbook - Volume 1 219 DAAC & Discipline Address User Support Office Contact Information 

Appendix A: EOS Distributed Active Archive Centers (DAACs) 

Contact Information Alaska SAR Facility (ASF) Sea Ice, Polar Processes, Synthetic Aperture Radar (SAR) Imagery Land Processes (LP) DAAC Land Processes GSFC Earth Sciences (GES) DAAC Upper Atmosphere, Global Biosphere, Atmo- spheric Dynamics

Physical Oceanography (PO) DAACPhysical OceanographyLangley Research Center (LaRC)Radiation Budget, Clouds, Aerosols, Tropospheric ChemistryNatoinal Snow and Ice Data Center (NSIDC)Snow and Ice, Cryosphere and ClimateOak Ridge National Laboratory (ORNL)Biogeochemical DynamicsSocioeconomic Data and Applications Center (EDAC)Human Impact on Global ChangeGlobal Hydrology Resource Center (GHRC)Lightning, Hydrologic Processes

Alaska SAR Facility, User ServicesPO Box 757320University of AlaskaFairbanks, AK 99775-7320United States Geological SurveyEROS Data CenterSioux Falls, SD 57198NASA/Goddard Space Flight CenterCode 902.2Greenbelt, MD 20771Physical Oceanography DAACM/S Raytheon-2994800 Oak Grove DrivePasadena, CA 91109NASA/Langley Research CenterMail Stop 157D2 South Wright StreetHampton, VA 23681-2199University of ColoradoCampus Box 449Boulder, CO 80309-0449Oak Ridge National Laboratory Environmental Sciences DivisionPO Box 2008, MS 6407, Bldg. 1507Oak Ridge, TN 37831-6407Socioeconomic Data and Applications CenterColumbia Univ./Lamont-Doherty Earth ObservatoryP.O. Box 1000, 61 Rt. 9WPalisades, NY 10964National Space Science and Technology Center320 Sparkman DriveHuntsville, AL 35805

Email: asf@eos.nasa.gov, uso@asf.alaska.eduPhone: 907-474-6166 Fax: 907-474-5195URL: www.asf.alaska.edu/Email: edc@eos.nasa.govPhone: 605-594-6116 Fax: 605-594-6963URL: edcdaac.usgs.govEmail: gsfc@eos.nasa.govPhone: 301-614-5224 Fax: 301-614-5268URL: daac.gsfc.nasa.gov/Email: jpl@eos.nasa.gov, podaac@podaac.jpl.nasa.govPhone: 626-744-5508 Fax: 626-744-5506URL: podaac.jpl.nasa.gov/Email: larc@eos.nasa.govPhone: 757-864-8656 Fax: 757-864-8807URL: eosweb.larc.nasa.gov/Email: nsidc@eos.nasa.gov, nsidc@kryos.colorado.eduPhone: 303-492-6199 Fax: 303-492-2468URL: www-nsidc.colorado.eduEmail: ornl@eos.nasa.gov, ornldaac@ornl.govPhone: 423-241-3952 Fax: 423-574-4665URL: www-eosdis.ornl.govEmail: ciesin.info@ceisin.columbia.eduPhone: 845-365-8920 Fax: 845-365-8922URL: sedac.ciesin.org, www.ciesin.orgEmail: ghrc@eos.nasa.govPhone: 256-961-7932 Fax: 256-961-7859URL: ghrc.msfc.nasa.gov/
220 Data Products Handbook - Volume 1 Appendix B: Points of Contact 

ASTER

Michael Abrams

Mail Stop 183-501

NASA/Jet Propulsion Laboratory 

4800 Oak Grove Drive 

Pasadena, CA 91109

Phone: 818-354-0937

Fax: 818-354-0966

Email: michael.abrams@jpl.nasa.gov

Ronald Alley

Mail Stop 183-501

NASA/Jet Propulsion Laboratory

4800 Oak Grove Drive

Pasadena, CA 91109

Phone: 818-354-0751

Fax: 818-354-0966

Email: ron.alley@jpl.nasa.gov

Alan Gillespie

University of Washington

Department of Earth and Space Sciences

Campus Box 35-1310

Seattle, WA 98195-1310

Phone: 206-685-8265

Fax: 206-685-2379

Email: alan@oz.geology.washington.edu

Frank Palluconi

Mail Stop 183-501

NASA/Jet Propulsion Laboratory

4800 Oak Grove Drive

Pasadena, CA 91109

Phone: 818-354-8362

Fax: 818-354-0966

Email: frank.d.palluconi@jpl.nasa.gov

Kurt Thome

University of Arizona

Optical Sciences Center 

Meinel Building 

P.O. Box 210094

Tucson, AZ 85721

Phone: 520-621-4535

Fax: 520-621-8292

Email: kurt.thome@opt-sci.arizona.edu 

Hiroshi Wantanabe

ERSDAC

Forefront Tower

3-12-1-Kachidoki

Chuo-ku Tokyo 104-0054 Japan

Phone: 011-81-33-533-9380

Fax: 011-81-33-533-9383

Email: wantanabe@ersdac.or.jp

Ronald Welch

South Dakota School of Mines and Technology

Institute of Atmospheric Science

Rapid City, SD 57701

Phone: 605-394-2291

Fax: 605-394-6061

Email: welch@cloud.ias.sdsmt.edu

CERES

Bruce R. Barkstrom

Mail Stop 420

NASA/Langley Research Center

Hampton, VA 23681

Phone: 757-864-5676

Fax: 757-864-7996 

Email: Bruce.R.Barkstrom@nasa.gov 

Thomas P. Charlock 

Mail Stop 420

NASA/Langley Research Center

Hampton, VA 23681

Phone: 757-864-5687 

Fax: 757-864-7996 

Email: Thomas.P.Charlock@nasa.gov 

David P. Kratz 

Mail Stop 420

NASA/Langley Research Center

Hampton, VA 23681

Phone: 757-864-5669 

Fax: 757-864-7996 

Email: David.P.Kratz@nasa.gov 

Appendix B: Points of Contact
Appendix B: Points of Contact Data Products Handbook - Volume 1 221

Robert B. Lee III 

Mail Stop 420

NASA/Langley Research Center

Hampton, VA 23681 

Phone: 757-864-5679 

Fax: 757-864-7996 

Email: Robert.B.Lee@nasa.gov 

Norman G. Loeb

Mail Stop 420

NASA/Langley Research Center

Hampton, VA 23681 

Phone: 757-864-5688

Fax: 757-864-7996 

Email: n.g.loeb@larc.nasa.gov 

Patrick Minnis 

Mail Stop 420

NASA/Langley Research Center

Hampton, VA 23681 

Phone: 757-864-5671 

Fax: 757-864-7996 

Email: Patrick.Minnis-1@nasa.gov 

Kory J. Priestley

Mail Stop 420

NASA/Langley Research Center

Hampton, VA 23681 

Phone: 757-864-8147

Fax: 757-864-7996 

Email: Kory.J.Priestley@nasa.gov 

David F. Young 

Mail Stop 420

NASA/Langley Research Center

Hampton, VA 23681 

Phone: 757-864-5740 

Fax: 757-864-7996 

Email: David.F.Young@nasa.gov 

Bruce A. Wielicki 

Mail Stop 420

NASA/Langley Research Center

Hampton, VA 23681 

Phone: 757-864-5683 

Fax: 757-864-7996 

Email: Bruce.A.Wielicki@nasa.gov 

Takmeng Wong 

Mail Stop 420

NASA/Langley Research Center

Hampton, VA 23681 

Phone: 757-864-5607

Fax: 757-864-7996

Email: Takmeng.Wong@nasa.gov 

DAS

Robert M. Atlas

Code 910.3 

NASA/Goddard Space Flight Center 

Greenbelt, MD 20771

Phone: 301-614-6140

Fax: 301-614-6297

Email: Robert.M.Atlas@nasa.gov

LIS

Dennis Boccippio

Code SD60

NASA/Marshall Space Flight Center

320 Sparkman Dr.

Huntsville, AL 35805

Phone: 256-961-7909

Fax: 256-961-7979

Email: dennis.J.boccippio@nasa.gov 

Hugh Christian

Code SD60

NASA/Marshall Space Flight Center

320 Sparkman Dr.

Huntsville, AL 35805

Phone: 256-961-7828

Fax: 256-961-7979

Email: Hugh.J.Christian@nasa.gov 

Steven Goodman

Code SD60

NASA/Marshall Space Flight Center

320 Sparkman Dr.

Huntsville, AL 35805

Phone: 256-961-7891

Fax: 256-961-7979

Email: Steven.J.Goodman@nasa.gov 

MISR

Carol Bruegge

Mail Stop 169-237

NASA/Jet Propulsion Laboratory

4800 Oak Grove Drive

Pasadena, CA 91109-8099

Phone: 818-354-4956

Fax: 818-393-4619

Email: carol.j.bruegge@jpl.nasa.gov
222 Data Products Handbook - Volume 1 Appendix B: Points of Contact 

Roger Davies

Mail Stop 169-237

NASA/Jet Propulsion Laboratory

4800 Oak Grove Drive

Pasadena, CA 91109-8099

Phone: 818-393-7236

Fax: 818-393-4619

Email: roger.davies@jpl.nasa.gov

David Diner

Mail Stop 169-237

NASA/Jet Propulsion Laboratory

4800 Oak Grove Drive

Pasadena, CA 91109-8099

Phone: 818-354-6319

Fax: 818-393-4619

Email: david.j.diner@jpl.nasa.gov

Ralph Kahn

Mail Stop 169-237

NASA/Jet Propulsion Laboratory

4800 Oak Grove Drive

Pasadena, CA 91109-8099

Phone: 818-354-9024

Email: ralph.kahn@jpl.nasa.gov

Fax: 818-393-4619

John Martonchik

Mail Stop 169-237

NASA/Jet Propulsion Laboratory

4800 Oak Grove Drive

Pasadena, CA 91109-8099

Phone: 818-354-2207

Fax: 818-393-4619

Email: john.v.martonchik@jpl.nasa.gov

MODIS

Mark Abbott

College of Oceanic & Atmospheric Sciences

Oceanography Administration Building 104

Oregon State University

Corvallis, OR 97331-5503

Phone: 541-737-4045

Fax: 541-737-2064

Email: mark@oce.orst.edu

Steven A. Ackerman 

Department of Atmospheric & Oceanic Sciences

University of Wisconsin Madison

1225 W. Dayton Street 

Madison, WI 53706 

Phone: 608-263-3647 

Fax: 608-262-5974 

Email: stevea@ssec.wisc.edu 

William B. Balch 

Bigelow Laboratory

McKown Point 

W. Boothbay Harbor, ME 04575

Phone: 207-633-9600 

Fax: 207-633-9641 

Email: bbalch@bigelow.org 

William L. Barnes

Code 970

NASA/Goddard Space Flight Center

Greenbelt, MD 20771

Phone: 301-614-5672

Fax: 301-614-5666

Email: William.L.Barnes@nasa.gov 

Otis Brown

RSMAS/MPO

University of Miami

4600 Rickenbacker Causeway

Miami, FL 33149-1098

Phone: 305-361-4000

Fax: 305-361-4622

Email: obrown@rsmas.miami.edu

Janet W. Campbell

Ocean Process Analysis Laboratory

Institute for the Study of Earth, Ocean, and Space

University of New Hampshire

Durham, NH 03824-2535

Phone: 603-862-1070

Fax: 603-862-0243

Email: janet.campbell@unh.edu

Kendall Carder

Department of Marine Science

University of South Florida

140 Seventh Avenue South

St. Petersburg, FL 33701-5016

Phone: 727-553-3952

Fax: 727-553-3918

Email: kcarder@monty.marine.usf.edu
Appendix B: Points of Contact Data Products Handbook - Volume 1 223

Dennis Clark

Code E/RA28

NOAA/NESDIS

5200 Auth Road, Room 105

Camp Springs, MD 20746-4304

Phone: 301-763-8102

Fax: 301-763-8020

Email: dennis.k.clark@noaa.gov

Wayne Esaias

Code 971

NASA/Goddard Space Flight Center

Greenbelt, MD 20771

Phone: 301-614-5709

Fax: 301-614-5644

Email: Wayne.E.Esaias@nasa.gov

Robert H. Evans

RSMAS/MPO

University of Miami

Meteorology & Physical Oceanography

4600 Rickenbacker Causeway

Miami, FL 33149-1098

Phone: 305-361-4799

Fax: 305-361-4622

Email: bob@rrsl.rsmas.miami.edu

Albert J. Fleig

Code 922.0

NASA/Goddard Space Flight Center

Greenbelt, MD 20771

Phone: 301-614-5498

Fax: 301-614-5269

Email: Albert.J.Fleig.1@gsfc.nasa.gov

Bo-Cai Gao

Remote Sensing Division, Code 7212

Naval Research Laboratory

4555 Overlook Drive, SW

Washington, DC 20375-5320

Phone: 202-767-8252

Fax: 202-404-8894

Email: bo-cai.gao@nrl.navy.mil

Howard R. Gordon

Department of Physics

University of Miami

Coral Cables, FL 33124

Phone: 305-284-2323

Fax: 305-284-4845

Email: gordon@phyvax.ir.miami.edu

Dorothy Hall

Code 974

NASA/Goddard Space Flight Center

Greenbelt, MD 20771

Phone: 301-614-5771

Fax: 301-614-5808

Email: Dorothy.K.Hall@nasa.gov

Frank E. Hoge

Building N-159 (West)

NASA/Wallops Flight Facility

Wallops Island, VA 23337

Phone: 757-824-1567

Fax: 757-824-1036

Email: Frank.E.Hoge@nasa.gov

Alfredo R. Huete

Department of Soil, Water, and Environmental 

 Sciences

University of Arizona

1200 E South Campus Drive

Shantz Building #38

Tuscon, AZ 85721-0038

Phone: 520-621-3228

Fax: 520-621-1647

Email: ahuete@ag.arizona.edu

Christopher O. Justice

Department of Geography

University of Maryland

College Park, MD 20742

Phone: 301-405-1600

Fax: 301-314-6503

Email: justice@hermes.geog.umd.edu

Yoram J. Kaufman

Code 913

NASA/Goddard Space Flight Center

Greenbelt, MD 20771

Phone: 301-614-6189

Fax: 301-614-6307

Email: Yoram.J.Kaufman@nasa.gov

Michael D. King

Code 900

NASA/Goddard Space Flight Center

Greenbelt, MD 20771

Phone: 301-614-5636

Fax: 301-614-5620

Email: Michael.D.King@nasa.gov
224 Data Products Handbook - Volume 1 Appendix B: Points of Contact 

Ricardo Letelier 

College of Oceanic & Atmospheric Sciences 

Oregon State University

104 Ocean Admin Bldg 

Corvallis, OR 97331-5503

Phone: 541-737-3890

Fax: 541-737-2064

Email: letelier@coas.oregonstate.edu

Edward J. Masuoka

Code 922

NASA/Goddard Space Flight Center

Greenbelt, MD 20771

Phone: 301-614-5515

Fax: 301-614-5269

Email: Edward.J.Masuoka@nasa.gov

W. Paul Menzel

NOAA/NESDIS

University of Wisconsin Madison

1225 W. Dayton Street

Madison, WI 53706

Phone: 608-264-5325

Fax: 608-262-5974

Email: paulm@ssec.wisc.edu

Peter J. Minnett

RSMAS/MPO

University of Miami

4600 Rickenbacker Causeway

Miami, FL 33149-1098

Phone: 305-361-4104

Fax: 305-361-4622

Email: pminnett@papaya.rsmas.miami.edu

Jan-Peter Muller

Department of Geomatic Engineering

University College London

Gower Street

London WC1E 6BT

United Kingdom

Phone: 44-20-7679-7227

Fax: 44-20-7380-0453

Email: jpmuller@ge.ucl.ac.uk

Ranga B. Myneni

Department of Geography

Boston University

Center for Remote Sensing

675 Commonwealth Avenue

Boston, MA 02215

Phone: 617-353-5742

Fax: 617-353-8399

Email: rmyneni@crsa.bu.edu

Steven Platnick

Code 913

NASA/Goddard Space Flight Center

Greenbelt, MD 20771

Phone: 301-614-6243

Fax: 301-614-6307

Email: steven.platnick@nasa.gov

Lorraine A. Remer

Code 913

NASA/Goddard Space Flight Center

Greenbelt, MD 20771

Phone: 301-614-6194

Fax: 301-614-6307

Email: Lorraine.A.Remer@nasa.gov

Steven W. Running

University of Montana

School of Forestry

Missoula, MT 59812

Phone: 406-243-6311

Fax: 406-243-4510

Email: swr@ntsg.umt.edu

Vincent V. Salomonson

Code 974

NASA/Goddard Space Flight Center

Greenbelt, MD 20771

Phone: 301-614-5631

Fax: 301-614-5808

Email: Vincent.V.Salomonson@nasa.gov

Alan H. Strahler

Department of Geography

Center for Remote Sensing

Boston University

725 Commonwealth Avenue

Boston, MA 02215

Phone: 617-353-5984

Fax: 617-353-8399

Email: alan@bu.edu

Didier Tanré 

Laboratoire dʼ Optique Atmosphérique

Université des Sciences et Techniques de Lille

Bat P5 

F-59655 Villeneuve dʼAscq Cedex 

France 

Phone: 33-3-20-33-70-33 

Fax: 33-3-20-43-43-42 

Email: tanre@loa.univ-lille1.fr
Appendix B: Points of Contact Data Products Handbook - Volume 1 225

Kurtis Thome

Remote Sensing Group/OCS

University of Arizona

1630 E. University Blvd

Tucson, AZ 85721-0094

Phone: 520-621-4535

Fax: 520-621-8292

Email: kurt.thome@opt-sci.arizona.edu

John R. G. Townshend

Department of Geography

University of Maryland at College Park

2181 LeFrak Hall

College Park, MD 20742-8225

Phone: 301-405-4050

Fax: 301-314-9299

Email: jt59@umail.umd.edu

Eric Vermote

Code 922

NASA/Goddard Space Flight Center

Greenbelt, MD 20771

Phone: 301-614-5521

Fax: 301-614-5269

Email: eric@kratmos.gsfc.nasa.gov

Kenneth Voss

Department of Physics

University of Miami

Coral Cables, FL 33124

Phone: 305-284-2323

Fax: 305-284-4222

Email: voss@phyvax.physics.miami.edu

Zhengming Wan

Institute for Computational Earth Systems Science

University of California

Santa Barbara, CA 93106-3060

Phone: 805-893-4541

Fax: 805-893-2578

Email: wan@icess.ucsb.edu

Robert E. Wolfe

Code 922.0

NASA/Goddard Space Flight Center

Greenbelt, MD 20771

Phone: 301-614-5508

Fax: 301-614-5269

Email: Robert.E.Wolfe.1@gsfc.nasa.gov

Xiaoxiong (Jack) Xiong

Code 920

NASA/Goddard Space Flight Center

Greenbelt, MD 20771

Phone: 301-867-2179

Fax: 301-614-6015

Email: Xiaoxiong.Xiong-1@nasa.gov

MOPITT

James Drummond

University of Toronto

Department of Physics

60 St. George Street

Toronto, Ontario M5S 1A7

Canada

Phone: 416-978-4723

Fax: 416-978-8905

Email: jim@atmosp.physics.utoronto.ca

David Edwards

National Center for Atmospheric Research

P.O. Box 3000

Boulder, CO 80307

Phone: 303-497-1857

Fax: 303-497-1492

Email: edwards@ucar.edu

John Gille

National Center for Atmospheric Research

P.O. Box 3000

Boulder, CO 80307

Phone: 303-497-8062

Fax: 303-497-2920

Email: gille@ucar.edu

TRMM

Robert Adler

Code 912

NASA/Goddard Space Flight Center

Greenbelt, MD 20771

Phone: 301-286-9086

Fax: 301-286-1762

Email: Robert.F.Adler@nasa.gov
226 Data Products Handbook - Volume 1 Appendix B: Points of Contact

William L. Barnes

Code 970

NASA/Goddard Space Flight Center

Greenbelt, MD 20771

Phone: 301-614-5672

Fax: 301-614-5666

Email: William.L.Barnes@nasa.gov 

Alfred Chang

Code 974

NASA/Goddard Space Flight Center

Greenbelt, MD 20771

Phone: 301-286-8997

Fax: 301-286-1758

Email: Alfred.T.Chang@nasa.gov

Robert Houze

Department of Atmospheric Sciences

University of Washington

ATG Bldg., Rm. 608, Code AK-40

Seattle, WA 98195

Phone: 206-543-6922

Fax: 206-543-0308

Email: houze@atmos.washington.edu

Christian Kummerow

Department of Atmospheric Science

Colorado State University

Ft. Collins, CO 80523

Phone: 970-491-7473

Fax: 970-491-8449

Email: kummerow@atmos.colostate.edu

Kenʼichi Okamoto

Director, Global Environment Division

Communications Research Laboratory

4-2-1 Nukui-Kita

Konegai, Tokyo 184

Japan

Phone: 0423-27-7541

Fax: 0423-27-6665

Email: okamoto@crl.go.jp

James Shiue

Code 975

NASA/Goddard Space Flight Center

Greenbelt, MD 20771

Phone: 301-286-6716

Fax: 301-286-1762

Email: James.C.Shiue@nasa.gov

Eric Smith

Code 912.1

NASA/Goddard Space Flight Center

Greenbelt, MD 20771

Phone: 301-286-5770

Fax: 301-286-1626

Email: Eric.A.Smith@nasa.gov

William L. Teng

Code 902

NASA/Goddard Space Flight Center

Greenbelt, MD 20771

Phone: 301-614-5164

Fax: 301-614-5268

Email: teng@daac.gsfc.nasa.gov
Appendix C: References Data Products Handbook - Volume 1 227

Abbott, M. R., P. J. Richerson, and T. M. Powell, 

1982: In situ response of phytoplankton fluorescence 

to rapid variations in light. Limn. Oceanog., 27, 218-

225.

Abbott, M. R., and R. M. Letelier, 1998: Decorrelation scales of chlorophyll as observed from bio-optical drifters in the California Current. Deep-Sea Res., 

45, 1639-1668.

Abrams, M., 2000: The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER): 

data products for the high spatial resolution imager 

on NASAʼs Terra platform. Int. J. Remote Sens., 

21(5), 847-859.

Abrams, M., S. Hook, and B. Ramachandran, 

2002: ASTER User Handbook, Version 2. NASA 

Jet Propulsion Laboratory, 135 pp. Available at: 

asterweb.jpl.nasa.gov/

Ackerman, S. A., K. I. Strabala, W. P. Menzel, R. A. 

Frey, C. C. Moeller, and L. E. Gumley, 1998: Discriminating clear sky from clouds with MODIS. J. 

Geophys. Res., 103, 32,141-32,157.

Adler, R. F., G. J. Huffman, D. T. Bolvin, S. Curtis, 

and E. J. Nelkin, 2000: Tropical rainfall distributions 

determined using TRMM combined with other satellite and rain gauge information. J. Appl. Meteor.,

39, 2007-2023.

Adler, R. F., G. J. Huffman, P. R. Keehn, and A. J. 

Negri, 1994: Global tropical rain estimates from microwave-adjusted geosynchronous IR data. Remote 

Sens. Rev., 11, 125-152.

Ahern, F., G. Goldammer, and C. O. Justice, Eds., 

2001: Global and Regional Vegetation Fire Monitoring From Space: Planning a Coordinated International Effort. SPB Academic Publishing, The Hague, 

The Netherlands.

Andreae, M. O., J. Fishman, M. Garstang, J. G. Goldammer, C. O. Justice, J. S. Levine, R. J. Sholes, 

B. J. Stocke, A. M. Thompson, B. VanWilgen, and 

STARE/TRACE-A SAFARI-92 Science Team, 1994: 

Biomass burning in the global environment. Global 

Atmospheric-Biospheric Chemistry, ed. by R. G. 

Prinn, Plenum Press, New York, 83-101.

Archer D., and E. Maier-Reimer, 1994: Effect of 

deep-sea sedimentary calcite preservation on atmospheric CO2

 concentration. Nature, 367, 260-263.

Armstrong, R. L., and M. J. Brodzik, 2002: Hemispheric–scale comparison and evaluation of passive-microwave snow algorithms. Ann. Glaciol., 

34, 38-44.

Asrar, G., and J. Dozier, 1994: Science Strategy for 

the Earth Observing System. American Institute of 

Physics Press, Woodbury, New York, 119 pp.

Asrar, G., and R. Greenstone, eds., 1995: MTPE/EOS 

Reference Handbook. NASA Pub. NP-215, National 

Aeronautics and Space Administration, Washington, 

D.C., 276 pp.

Asrar, G., J. F. Creedon, R. S. Estes, A. V. Diaz, H. 

McDonald, K. Pedersen, and E. Stone, Eds., 2001: 

Earth Science Enterprise Strategic Plan. NASA 

Headquarters.

Atlas, D., D. Rosenfeld, and D. A. Short, 1990: The 

estimation of convective rainfall by area integrals, 

Part 1 the theoretical and empirical basis. J. Geophys. 

Res., 95, 2153-2160.

Atlas, R., R. N. Hoffman, S. M. Leidner, J. Sienkiewicz, T.-W. Yu, S. C. Bloom, E. Brin, J. Ardizzone, J. 

Terry, D. Bungato, and J. C. Jusem, 2001: The effects 

of marine winds from scatterometer data on weather 

analysis and forecasting. Bull. Amer. Meteor. Soc., 

82, 1965-1990.

Austin, R. W., 1974: Inherent spectral radiance signals of the ocean surface. In Ocean Color Analysis, 

SIO ref. 74-10, Scripps Inst. Oceanog., La Jolla, CA, 

2.1-2.20.

Appendix C: References
228 Data Products Handbook - Volume 1 Appendix C: References 

Avis, L. M., R. N. Green, J. T. Suttles, and S. K. 

Gupta, 1984: A robust pseudoinverse spectral filter 

applied to the Earth Radiation Budget Experiment 

(ERBE) scanning channels. NASA Tech. Memo. TM85781, 33 pp.

Awaka, J., T. Iguchi, and K. Okamoto, 1998: Early 

results on rain type classification by the Tropical 

Rainfall Measuring Mission (TRMM) precipitation 

radar. Proc. 8th URSI Commission F Open Symp., 

Aveiro, Portugal, 143-146.

Balch, W. M., P. M. Holligan, S. G. Ackleson, and 

K. J. Voss, 1991: Biological and optical properties 

of mesoscale coccolithophore blooms in the Gulf of 

Maine. Limn. Oceanog., 34, 629-643.

Balch, W. M., K. A. Kilpatrick, P. M. Holligan, and 

C. Trees., 1996a: The 1991 coccolithophore bloom 

in the central north Atlantic. I. Optical properties and 

factors affecting their distribution. Limnol. Oceanogr., 41, 1669-1683.

Balch, W. M., D. Drapeau, J. Fritz, B. Bowler, and 

J. Nolan, 2001: Optical backscattering in the Arabian Sea-continuous underway measurements of 

particulate inorganic and organic carbon. Deep-Sea 

Res., I48, 2423-2452.

Balch, W. M., D. T. Drapeau, T. L. Cucci, R. D. Vaillancourt, K. A. Kilpatrick, and J. J. Fritz., 1999: Optical backscattering by calcifying algae—Separating 

the contribution by particulate inorganic and organic 

carbon fractions. J. Geophys. Res., 104, 1541-1558.

Balch, W. M., K. Kilpatrick, P. M. Holligan, D. 

Harbour, and E. Fernandez., 1996b: The 1991 coccolithophore bloom in the central north Atlantic. II. 

Relating optics to coccolith concentration. Limnol. 

Oceanogr., 41, 1684-1696.

Barkstrom, B. R., and G. L. Smith, 1986: The Earth 

radiation budget experiment: Science and implementation. Rev. Geophys., 24, 379-390.

Barkstrom, B. R., and B. A. Wielicki, 1995: Clouds 

and the Earthʼs Radiant Energy System (CERES) 

Algorithm Theoretical Basis Document. Volume I - 

Overviews, Subsystem 0 - CERES Data Processing 

System Objectives and Architecture, NASA Ref. Pub. 

1376, vol. I, 21-97.

Barnes, R. A., W. L. Barnes, C. Lyu, and J. M. Gales, 

2000: An overview of the visible and infrared scanner 

radiometric calibration algorithm. J. Atmos. Oceanic 

Technol., 17, 395-405.

Barnes, W. L., T. S. Pagano, and V. V. Salomonson, 

1998: Prelaunch characteristics of the Moderate 

Resolution Imaging Spectroradiometer (MODIS) 

on EOS-AM1. IEEE Trans. Geosci. Remote Sens., 

36, 1088.

Barnsley, M. J., and J.-P. Muller, 1991: Measurement, 

simulation and analysis of directional reflectance 

properties of Earth surface materials. ESA SP-319, 

375-382.

Barry, R. G., and A. L. Varani, eds., 1995: Earth Observing Science: Highlights 1994, NSIDC Distributed 

Active Archive Center, Boulder, CO.

Becker, F., 1987: The impact of spectral emissivity on 

the measurement of land surface temperature from a 

satellite. Int. J. Remote Sens., 8(10), 1509-1522.

Behrenfeld, M. J., and P. G. Falkowski, 1997a: Photosynthetic rates derived from satellite-based chlorophyll concentration. Limnol. Oceanogr., 42, 1-20. 

Behrenfeld, M. J., and P. G. Falkowski, 1997b: A 

consumerʼs guide to phytoplankton primary productivity models. Limnol. Oceanogr., 42, 1479-1491. 

Bidigare, R. R., M. E. Ondrusek, J. H. Morrow, and 

D. A. Kiefer, 1990: In vivo absorption properties of 

algal pigments. SPIE Vol. 1302, Ocean Optics X, 

290-302.

Boccippio, D. J., K. T. Driscoll, J. M. Hall, and D. E. 

Buechler, 1998: LIS/OTD Software Guide, available 

at thunder.nssta.nasa.gov/data.html.

Boccippio, D. J., K. T. Driscoll, W. J. Koshak, R. J. 

Blakeslee, W. L. Boeck, D. A. Mach, D. E. Buechler, 

H. J. Christian, and S. J. Goodman, 2000: The Optical 

Transient Detector (OTD): Instrument characteristics 

and cross-sensor validation. J. Atmos. Oceanic Technol., 17, 441-458.

Boccippio, D. J., S. J. Goodman, and S. Heckman, 

2000: Regional differences in tropical lightning distributions. J. Appl. Meteor., 39, 2231-2248.

Boccippio, D. J., W. J. Koshak, and R. J. Blakeslee, 

2002: Performance assessment of the Optical Transient Detector and Lightning Imaging Sensor:1. Predicted diurnal variability. J. Atmos. Oceanic Technol., 

19, 1318-1332.
Appendix C: References Data Products Handbook - Volume 1 229

Bruegge, C. J., N. L. Chrien, R. R. Ando, D. J. Diner, 

W. A. Abdou, M. C. Helmlinger, S. H. Pilorz, and K. J. 

Thome, 2002: Early validation of the Multi-angle Imaging SpectroRadiometer (MISR) radiometric scale. 

IEEE Trans. Geosci. Remote Sens., 40, 1477-1492.

Bruegge, C. J., V. G. Duval, N. L. Chrien, R. P. 

Korechoff, B. J. Gaitley, and E. B. Hochberg, 1998. 

MISR prelaunch instrument calibration and characterization results. IEEE Trans. Geosci. Remote Sens.

36, 1186-1198.

Buechler, D. E., K. T. Driscoll, S. J. Goodman, and 

H. J. Christian, 2000: Lightning activity within 

a tornadic thunderstorm observed by the Optical 

Transient Detector (OTD). Geophys. Res. Lett., 27, 

2253-2256.

Campbell, J. W., D. Antoine, R. Armstrong, K. Arrigo, 

W. Balch, R. Barber, M. Behrenfeld, R. Bidigare, J. 

Bishop, M.-E. Carr, W. Esaias, P. Falkowski, N. Hoepffner, R. Iverson, D. Kiefer, S. Lohrenz, J. Marra, A. 

Morel, J. Ryan, V. Vedernikov, K. Waters, C. Yentsch, 

and J. Yoder, 2002: Comparison of algorithms for 

estimating ocean primary productivity from surface 

chlorophyll, temperature, and irradiance. Global 

Biogeochem. Cycles, in press.

Capone, D. G., J. P. Zehr, H. W. Paerl, B. Bergman, 

and E. J. Carpenter, 1997: Trichodesmium: A globally significant marine cyanobacterium. Science, 276, 

1221-1229. 

Carder, K., 2001: Chrlorophyll a (Semi-Analytic), 

PAR, total Absobption Coefficient, MOD 21, 22, 36, 

39 Status. Powerpoint Presentation, MODIS Science 

Team Meeting, BWI Marriott, December 17-19, 

2001. modis-ocean.gsfc.nasa.gov/refs.html

Carder, K., 2002: Performance of MODIS Semianalytic Ocean Color Algorithms: Chlorophyll a, 

Absorption Coefficients, and Absorbed Radiation 

by Phytoplankton, Powerpoint Presentation, MODIS 

Science Team Meeting, Greenbelt, MD, July 22-24, 

2002. modis-ocean.gsfc.nasa.gov/refs.html.

Carder, K. L., F. R. Chen, J. P. Cannizzaro, J. W. 

Campbell, and B. G. Mitchell, 2003: Performance of 

the MODIS semi-analytical ocean color algorithm for 

chlorophyll-a. Adv. Space Res., in press.

Boccippio, D. J., and coauthors, 2000b: The Optical 

Transient Detector (OTD): Instrument characteristics 

and cross-sensor validation. J. Atmos. Oceanic Technol., 17, 441-458.

Bohme, R., 1991-1993: Inventory of world topographic mapping. In three volumes on behalf of the 

International Cartographic Association, Elsevior Applied Science Publishers, New York, 1070 pp.

Borak, J. S., and A. H. Strahler, 1999: Feature selection and land cover classification of a MODIS-like 

data set for a semiarid environment. Int. J. Remote 

Sens., 20, 919-938. 

Bothwell, G. W., E. G. Hansen, R. E. Vargo, and K. 

C. Miller, 2002: The Multi-angle Imaging SpectroRadiometer science data system, its products, tools, 

and performance. IEEE Trans. Geosci. Remote Sens., 

40, 1467-1476.

Brasseur, G. P., and R. G. Prinn, 1992: Biogenic 

and anthropogenic trace gases in the atmosphere. 

The Use of EOS for Studies of Atmospheric Physics, 

ed. by J. C. Gille and G. Visconti, North Holland, 

Amsterdam, 45-64.

Breigleb, B. P., and V. Ramanathan, 1982: Spectral 

and diurnal variations in clear-sky planetary albedo.

J. Climate Appl. Meteor., 21, 1168-1171.

Brenninkmeijer, C. A. M., M. R. Manning, D. C. 

Lowe, G. Wallace, R. J. Sparks, and A. Volz-Thomas, 

1992: Interhemispheric asymmetry in OH abundance 

inferred from measurements of atmospheric CO. Nature, 356, 50-52.

Brest, C. L., and S. N. Goward, 1987: Deriving surface albedo measurements from narrow band satellite 

data. Int. J. Remote Sens., 8, 351-367.

Brooks, D. R., E. F. Harrison, P. Minnis, J. T. Suttles, 

and R. S. Kandel, 1986: Development of algorithms 

for understanding the temporal and spatial variability 

of the Earthʼs radiation balance. Rev. Geophys., 24, 

422-438.

Brown, O., R. Evans, P. Minnett, E. Kearns, and K. 

Kilpatrick, 2002: Sea Surface Temperature measured by the Moderate Resolution Imaging Spectroradiometer (MODIS) (PowerPoint presentation), EOS 

IWG, Ellicott City, MD.
230 Data Products Handbook - Volume 1 Appendix C: References 

Carder, K. L, F. R. Chen, Z. P. Lee, S. K. Hawes, 

and D. Kamykowski, 1999: Semianalytic Moderate-Resolution Imaging Spectrometer algorithms 

for chlorophyll a and absorption with bio-optical 

domains based on nitrate-depletion temperatures. J. 

Geophys. Res., 104(C3), 5403-5421.

Carder, K. L., R. G. Steward, J. H. Paul, and G. A. 

Vargo, 1986: Relationship between chlorophyll and 

ocean color constituents as they affect remote-sensing 

reflectance models. Limn. Oceanog., 31, 403-413.

Carder, K. L., S. K. Hawes, K. A. Baker, R. C. Smith, 

R. G. Steward, and B. G. Mitchell, 1991a: Reflectance 

model for quantifying chlorophyll a in the presence 

of productivity degradation products. J. Geophys. 

Res., 96, 599-611.

Carder, K. L., W. W. Greg, D. K. Costello, K. Haddad, 

and J. M. Prospero, 1991b: Determination of Saharan 

dust radiance and chlorophyll from CZCS imagery. 

J. Geophys. Res., 96, 5369-5378.

Carroll, T. R., 1995: Remote sensing of snow in the 

cold regions. Proceedings of the First Moderate Resolution Imaging Spectroradiometer (MODIS) Snow 

and Ice Workshop, 13-14 September, 1995, Greenbelt, 

MD, NASA Conf. Pub. 3318, 3-14.

Cavalieri, D. J., J. Maslanik, T. Markus, J. Stroeve, 

M. Sturm, J. Heinrichs, E. Kim, A. J. Gasiewski, and 

J. C. Comiso, 2002: EOS Aqua AMSR-E Arctic sea 

ice validation program. Proceedings of the International Society for Optical Engineering, Symposium 

on Remote Sensing of the Atmosphere, Ocean, Environment, and Space, 23-27 October 2002, Hangzhou, 

China.

Caylor I. J., G. M. Heymsfield, R. Meneghini, and 

L. S. Miller, 1997: Correction of sampling errors in 

ocean surface cross-sectional estimates from nadirlooking weather radar. J. Atmos. Oceanic Technol., 

14, 203-210. 

Cess, R., E. Dutton, J. DeLuisi, and F. Jiang, 1991: 

Determining surface solar absorption from broadband 

satellite measurements for clear skies: Comparisons 

with surface measurements. J. Climatol., 4, 236-

247.

Chahine, M. T., 1980: Infrared remote sensing of 

sea surface temperature. In Remote Sensing of Atmospheres and Oceans, ed. by A. Deepak, Academic 

Press, New York, 411-435.

Chambers, L. H., B. Lin, and D. F. Young, 2002: 

Examination of new CERES data for evidence of 

tropical Iris feedback. J. Climate, 15, 3719-3726.

Chamberlin, W. S., and J. Marra, 1992: Estimation 

of photosynthetic rate from measurements of natural fluorescence: Analysis of the effects of light and 

temperature. Deep-Sea Res., 39, 1695-1706.

Chang, A. T. C., L. S. Chiu, C. Kummerow, and J. 

Meng, 1999a: First results of the TRMM Microwave 

Imager (TMI) monthly oceanic rain rate: Comparison 

with SSM/I. Geophys. Res. Lett., 26, 2379-2382.

Chang, A. T. C., L. S. Chiu, 1999b: Nonsystematic 

Errors of Monthly Oceanic Rainfall Derived from 

SSM/I. Mon. Wea. Rev., 127, 1630–1638.

Chang, A. T. C., R. E. J. Kelly, J. L. Foster, and D.K. 

Hall, 2002: Estimation of global snow cover using 

passive microwave data. Proceedings of the International Society for Optical Engineering, Symposium 

on Remote Sensing of the Atmosphere, Ocean, Environment, and Space, 23-27 October 2002, Hangzhou, 

China.

Charlock, T. P., and T. L. Alberta, 1996: The 

CERES/ARM/GEWEX Experiment (CAGEX) for 

the retrieval of radiative fluxes with satellite data. 

Bull. Amer. Meteor. Soc., 77, 2673-2683.

Charlock, T. P., F. Rose, S.-K Yang, T. Alberta, and 

G. Smith, 1993: An observation study of the interaction of clouds, radiation, and the general circulation. Proceedings of the IRS ʼ92: Current Problems 

in Atmospheric Radiation. Tallinn (3-8 August 1992), 

A. Deepak Publishing, 151-154.

Charlock, T. P., D. Rutan, G. L. Smith, F. G. Rose, 

T. L. Alberta, N. Manalo-Smith, L. H. Coleman, D. 

P. Kratz, T. D. Bess, and K. A. Bush, 1995: Clouds 

and the Earthʼs Radiant Energy System (CERES) 

Algorithm Theoretical Basis Document. Volume IV 

- Determination of Surface and Atmosphere Fluxes 

and Temporally and Spatially Averaged Products 

(Subsystems 5-12), Subsystem 5.0 - Compute Surface and Atmospheric Fluxes, NASA Ref. Pub. 1376, 

Vol. IV, 1-52.

Chen, Y., S. Sun-Mack, P. Minnis, D. F. Young, and 

W. L. Smith, Jr., 2002: Surface spectral emissivity 

derived from MODIS data. Proc. SPIE 3rd Intl. AsiaPacific Environ. Remote Sens. Symp. 2002: Remote 

Sensing of Atmosphere, Ocean, Environment, and 

Space, Hangzhou, China, October 23-27.
Appendix C: References Data Products Handbook - Volume 1 231

Chicano, R. J., and R. S. Oremland, 1988: Biogeochemical aspects of atmospheric methane. Global 

Biogeochem. Cycles, 2, 299-327.

Chiu, L. S., 1988: Rain estimation from space 

observations: Areal rainfall-rain area relationship. 

Paper presented at the Third Conference on Satellite 

Meteorology and Oceanography of the American 

Meteorological Society, Feb. 1-5, 1988, Anaheim, 

CA. Preprint Volume, pp. 3663-3668.

Chiu, L. S., A. Chang, J. Janowiak, 1993: Comparison of Monthly Rain Rates Derived from GPI and 

SSM/I Using Probability Distribution Functions. J. 

Appl. Meteor., 32, 323-334.

Chomko, R., and H. R. Gordon, 1998: Atmospheric 

correction of ocean color imagery: Use of the Junge 

power-law aerosol size distribution with variable 

refractive index to handle aerosol absorption. Appl. 

Opt., 37, 5560-5572.

Christian, H. J., R. J. Blakeslee, D. J. Boccippio, W. 

L. Boeck, D. E. Buechler, K. T. Driscoll, S. J. Goodman, J. M. Hall, W. J. Koshak, D. M. Mach, and M. 

F. Stewart, 2003: Global frequency and distribution 

of lightning as observed by the Optical Transient 

Detector. J. Geophys. Res., 108(D1), 4004, doi:

10.1029/2002JD002347.

Christian, H. J., R. J. Blakeslee, S. J. Goodman, 

D. A. Mach, M. F. Stewart, D. E. Buechler, W. J. 

Koshak, J. M. Hall, K. T. Driscoll, and D. J. Boccippio, 1999: The Lightning Imaging Sensor, poster 

presented at Proc. 11th International Conference on 

Atmospheric Electricity (NASA), Guntersville, AL, 

7-11 June, 746-749.

Christian, H. J., R. J. Blakeslee, and S. J. Goodman, 

1989: The detection of lightning from geostationary 

orbit. J. Geophys. Res., 94, 13,329-13,337.

Christian, H. J., R. J. Blakeslee, S. J. Goodman, 

and D. M. Mach, eds., 2000: Algorithm Theoretical 

Basis Document (ATBD) for the Lightning Imaging 

Sensor, 53 pp.

Chu, D. A., Y. J. Kaufman, C. Ichoku, L. A. Remer, D. 

Tanré, and B. N. Holben, 2002: Validation of MODIS 

aerosol optical depth retrieval over land. Geophys. 

Res. Lett., 29(12), doi: 10.1029/2001GL013205.

Chu, D. A., Y. J. Kaufman, L. A. Remer, and B. N. 

Holben, 1998: Remote sensing of smoke from MODIS 

airborne simulator during the SCAR-B experiment. J. 

Geophys. Res., 103(D24), 31,979-31,987.

Churchill, D. D., and R. A. Houze, Jr., 1984: Development and structure of winter monsoon cloud 

clusters on 10 December 1978. J. Atmos. Sci., 41, 

933-960.

Cicerone, R. J., and R. S. Oremland, 1988: Biogeochemical aspects of atmospheric methane. Global 

Biogeochem. Cycles, 2, 299-327.

Cihlar, J. C., H. Ly, Z. Li, J. Chen, H. Pokrant, and F. 

Huang, 1997: Mult-temporal, multichannel AVHRR 

data sets for land biosphere studies—Artifacts and 

corrections. Remote Sens. Environ., 60, 35-57.

Cohen, W. B., and C. O. Justice, Eds. 1999: Validating 

MODIS terrestrial ecology products: linking in situ

and satellite measurements. Special Edition, Remote 

Sens. Environ., 70, 1-3.

Crutzen, P. J., 1991: Methaneʼs sinks and sources. 

Nature, 350, 380-381.

Culver, M. E., and M. J. Perry, 1994: Detection of 

phycoerythrin fluorescence in upwelling irradiance 

spectra. Eos, Trans. Amer. Geophys. Union, 75, 

233.

dʼEntremont, R. E., C. L. Barker Schaaf, W. Lucht, 

and A. H. Strahler, 1999: Retrieval of red spectral 

albedo and bidirectional reflectance using AVHRR 

HRPT and GOES satellite observations of the New 

England region. J. Geophys. Res., D-104, 6229-

6239.

De Abreu, R. A., J. Key, J. A. Maslanik, M. C. 

Serreze, and E. F. LeDrew, 1994: Comparison of 

in situ and AVHRR-derived broadband albedo over 

Arctic sea ice. Arctic, 47(3), 288-297.

Deeter, M. N., G. L. Francis, D. P. Edwards, J. C. 

Gille, E. McKernan, and J. R. Drummond, 2002: 

Operational validation of the MOPITT instrument 

optical filters. J. Atmos. Oceanic Tech., 19(11), 

1772-1782.

DeFries, R. S., M. C. Hansen, J. R. G. Townshend, 

and A. C. Janetos, 2000: A new global 1-km dataset 

for percentage tree cover derived from remote sensing. Global Change Biology, 6, 247-254.

Del Genio, A. D., and W. Kovari, 2002: Climatic 

properties of tropical precipitating convection under 

varying environmental conditions. J. Climate, 15, 

2597-2615.
232 Data Products Handbook - Volume 1 Appendix C: References 

Deschamps, P. Y., M. Herman, and D. Tanré, 1983: 

Modeling of the atmospheric effects and its application to the remote sensing of ocean color. Appl. 

Opt., 22, 3751-3758.

Deschamps, P. Y., and T. Phulpin, 1980: Atmospheric 

correction of infrared measurements of sea surface 

temperature using channels at 3.7, 11, and 12 μm. 

Bound.-Layer Meteor., 18, 131-143.

Di Girolamo, L., and R. Davies, 1994: A banddifferenced angular signature technique for cirrus 

cloud detection. IEEE Trans. Geosci. Remote Sens., 

36, 890-896.

Dickerson, R. R., G. J. Huffman, W. T. Luke, L. J. 

Nunnermacker, K. E. Pickering, A. C. D. Leslie, C. 

G. Lindsey, W. G. N. Slinn, T. J. Kelly, P. H. Daum, 

A. C. Delany, J. P. Greenberg, P. R. Zimmerman, J. F. 

Boatmen, J. D. Ray, and D. H. Stedman, 1987: Thunderstorms: an important mechanism in the transport 

of air pollutants. Science, 235, 460.

Diner, D. J., W. A. Abdou, C. J. Bruegge, J. E. Conel, 

K. A. Crean, B. J. Gaitley, M. C. Helmlinger, R. A. 

Kahn, J. V. Martonchik, S. H. Pilorz, and B.N. Holben, 2001: MISR aerosol optical depth retrievals over 

southern Africa during the SAFARI-2000 dry season 

campaign. Geophys. Res. Lett., 28, 3127-3130.

Diner, D. J., G. P. Asner, R. Davies, Y. Knyazikhin, 

J-P. Muller, A. W. Nolin, B. Pinty, C. B. Schaaf, and 

J. Stroeve, 1999: New directions in Earth observing: 

Scientific applications of multiangle remote sensing. 

Bull. Amer. Meteor. Soc., 80, 2209-2228.

Diner, D. J., J. C. Beckert, G. W. Bothwell, and J. I. 

Rodriguez, 2002: Performance of the MISR instrument during its first 20 months in Earth orbit. IEEE 

Trans. Geosci. Remote Sens., 40, 1449-1466.

Diner, D. J., J. C. Beckert, T. H. Reilly, C. J. Bruegge, 

J. E. Conel, R. A. Kahn, J. V. Martonchik, T. P. Ackerman, R. Davies, S. A. W. Gerstl, H. R. Gordon, J 

-P. Muller, R. B. Myneni, P. J. Sellers, B. Pinty, and 

M. M. Verstraete, 1998: Multi-angle Imaging SpectroRadiometer (MISR) instrument description and 

experiment overview. IEEE Trans. Geosci. Remote 

Sens., 36, 1072-1087.

Dlugokencky, E. J., L. P. Steele, P. M. Lang, and 

K. A. Masarie, 1994: The growth rate and distribution of atmospheric methane. J. Geophys. Res., 99, 

17,021-17,043.

Dong, X., P. Minnis, G. G. Mace, E. E. Clothiaux, 

C. Long, and S. Sun-Mack, 1999: Validation of 

CERES/VIRS cloud properties using ground-based 

measurements obtained at the DOE ARM sites. Proc. 

AMS 10th Conf. Atmos. Rad., Madison, WI, June 28-

July 2, 29-32.

Dozier, J., and Z. Wan, 1994: Development of practical multiband algorithms for estimating land-surface 

temperature from EOS-MODIS data. Adv. Space Res., 

13(3), 81-90.

Drummond, J. R., 1992: Measurements of pollution in 

the troposphere (MOPITT). The Use of EOS for Studies of Atmospheric Physics, ed. by J.C. Gille and G. 

Visconti, North Holland, Amsterdam, pp. 77-101.

Drummond, J. R. and G. S. Mand, 1996: The 

Measurements of Pollution in the Troposphere 

(MOPITT) Instrument: Overall Performance and 

Calibration Requirements. J. Atmos. Oceanic Technol., 13, 314.

Dubovik, O., B. N. Holben, T. F. Eck, A. Smirnov, Y. 

J. Kaufman, M. D. King, D. Tanré, and I. Slutsker, 

2002: Variability of absorption and optical properties 

of key aerosol types observed in worldwide locations. 

J. Atmos. Sci., 59, 590-608.

Ebert, E., 1987: A pattern recognition technique for 

distinguishing surface and cloud types in the polar 

regions. J. Climate Appl. Meteor., 26, 1412- 1427.

Edwards, D. P., C. M. Halvorson, and J. C. Gille, 

1999: Radiative transfer modeling for the EOS 

Terra satellite Measurement of Pollution in the Troposphere (MOPITT) instrument. J. Geophys. Res., 

104, 16755. 

Edwards, D. P., 2002: The MOPITT Instrument on 

Terra: Mission and Data Processing Status, SWAMP, 

Nov. 2002 (presentation), www.eos.ucar.edu/mopitt/

pubs/index.html.

Eppley, R. W., E. Stewart, M. R. Abbott, and U. 

Heyman, 1985: Estimating ocean primary production 

from satellite chlorophyll: Introduction to regional 

differences and statistics for the Southern California 

Bight. J. Plank. Res., 7, 57-70.

Esaias, W. E., M. R. Abbott, I. Barton, O. B. Brown, 

J. W. Campbell, K. L. Carder, D. K. Clark, R. H. 

Evans, F. E. Hoge, H. R. Gordon, W. M. Balch, 

R. Letelier, and P. J. Minnett, 1998: An overview 

of MODIS capabilities for ocean science observations. IEEE Trans. Geosci. Remote Sens., 36(4), 

1250-1265.
Appendix C: References Data Products Handbook - Volume 1 233

Esaias, W. E., R. L. Iverson, and K. Turpie, 1999: 

Ocean province classification using ocean colour 

data: Observing biological signatures of variations 

in physical dynamics. Global Change Biology, 5, 

1-17.

Evans, R. H., and H. R. Gordon, 1994: CZCS System 

Calibration: A retrospective examination. J. Geophys. 

Res., 99C, 7293-7307.

Falkowski, P. G., and J. A. Raven, 1997: Aquatic 

Photosynthesis, Blackwell Science, Malden, MA., 

375 pp.

Field, C. B., J. T. Randerson, and C. M. Malstrom, 

1995: Global net primary production: Combining 

ecology and remote sensing. Remote Sens. Environ., 

51, 74-88.

Fitzwater, S. E., G. A. Knauer, and J. H. Martin, 

1982: Metal contamination and its effect on primary 

production measurements. Limn. Oceanog., 27, 

544-551.

Friedl, M. A., and C. E. Brodley, 1998: Decision tree 

classification of land cover from remotely sensed 

data. Remote Sens. Environ., 61, 399-409.

Friedl, M. A., C. E. Brodley, and A. H. Strahler, 1999: 

Maximizing land cover classification accuracies produced by decision trees at continental to global scales. 

IEEE Trans. Geosci. Remote Sens., 37, 969-977.

Friedl, M., D. Muchoney, D. McIver, F. Gao, J. Hodges, and A. Strahler, 2000: Characterization of North 

American Land Cover from NOAA-AVHRR Data 

Using the EOS-MODIS Land Cover Classification 

Algorithm. Geophys. Res. Lett., 27, 977-980. 

Fu, Q., and K. Liou, 1993: Parameterization of the 

radiative properties of cirrus clouds. J. Atmos. Sci., 

50, 2008-2025.

Fujisada, H., 1994: Overview of ASTER Instruments 

on EOS-AM1 Platform. Proc. SPIE, 2268, 14-36.

Fujisada, H., 1998: ASTER Level-1 Processing 

Algorithm. IEEE Trans. Geosci. Remote Sens., 36, 

1101-1112.

Furuhama, Y., T. Ihara, M. Fujita, T. Shinozuka, 

K. Nakamura, and J. Awaka, 1982: Propagation 

characteristics of millimeter and centimeter waves 

of ETS-II classified by rainfall types. Annales des 

Telecom., 36, 24-32.

Gao, B. C., and A. F. H. Goetz, 1990: Column atmospheric water vapor and vegetation liquid water 

retrievals from airborne imaging spectrometer data. 

J. Geophys. Res., 95, 3549-3564.

Gao, B. C., A. F. H. Goetz, and W. J. Wiscombe, 

1993a: Cirrus cloud detection from airborne imaging 

spectrometer data using the 1.38 micron water vapor 

band. Geophys. Res. Lett., 4, 301-304.

Gao, B. C., K. D. Heidebrecht, and A. F. H. Goetz, 

1993b: Derivation of scaled surface reflectances from 

AVIRIS data. Remote Sens. Environ., 44, 165-178.

Gao, B. C., and Y. J. Kaufman, 2003: Water vapor 

retrievals from images of MODIS near-IR channels. 

J. Geophys. Res., 108(D13), 4389.

Gao, B. C., Y. J. Kaufman, W. Han, and W. J. Wiscombe, 1998: Correction of thin cirrus path radiance 

in the 0.4-1.0 μm spectral region using the sensitive 

1.375-μm cirrus detecting channel. J. Geophys. Res., 

103, 32,169-32,176.

Gao, B. C., Y. J. Kaufman, D. Tanré, and R.-R. Li, 

2002b: Distinguishing tropospheric aerosols from thin 

cirrus clouds for improved aerosol retrievals using the 

ratio of 1.38-mm and 1.24-mm channels. Geophys. 

Res. Lett., 29, 1890, 10.1029/2002GL015475.

Gao, B. C., P. Yang, W. Han, R.-R. Li, and W. J. 

Wiscombe, 2002a: An algorithm using visible and 

1.38-μm channels to retrieve cirrus cloud reflectances 

from aircraft and satellite data. IEEE Trans. Geosci. 

Remote Sens., 40, 1659-1668.

Gao, B. C., P. Yang, R.-R. Li, S. K. Park, and W. J. 

Wiscombe, 2003: Detection of high clouds in polar 

regions during the daytime using the MODIS 1.375-

micron channel. IEEE Trans. Geosci. Remote Sens., 

41(2), 474-481.

Gao, F., C. Schaaf, A. H. Strahler, and W. Lucht, 

2001: Using a multi-kernel least variance approach 

to retrieve and evaluate albedo from limited BRDF 

observations. Remote Sens. Environ., 76, 57-66. 

Gao, F., X. Li, A. H. Strahler, and C. Schaaf, 2001: 

Evaluation of the LiTransit Kernel for BRDF Modeling. Remote Sens. Rev., 19, 205-224.

Gao, X., A. R. Huete, W. Ni, and T, Miura, 2000: Optical-biophysical relationships of vegetation spectra 

without background contamination. Remote Sens. 

Environ., 74, 609-620.
234 Data Products Handbook - Volume 1 Appendix C: References 

Garver, S. A., and D. A. Siegel, 1997: Inherent optical property inversion of ocean color spectra and its 

biogeochemical interpretation. 1. Time series from 

the Sargasso Sea. J. Geophys. Res., 102, 18,607-

18,625.

Giglio L., J. D. Kendall, and C. O. Justice, 1999: 

Evaluation of global fire detection algorithms using 

simulated AVHRR infrared data. Int. J. Remote Sens., 

20, 1947-1985.

Gillespie, A. R., A. B. Kahle, and R. E. Walker, 1986: 

Color enhancement of highly correlated images. I. 

Decorrelation and HSI contrast stretches. Remote 

Sens. Environ., 20, 209-235.

Gillespie, A., S. Rokugawa, T. Matsunaga, J. S. 

Cothern, S. Hook, and A. B. Kahle, 1998: A temperature and emissivity separation algorithm for 

Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images. IEEE Trans. 

Geosci. Remote Sens., 36(4), 1113-1126.

Goodman, A. H., and A. Henderson-Sellers, 1988: 

Cloud detection analysis: A review of recent progress. 

Atmos. Res., 21, 203.

Goodman, S. J., D. E. Buechler, K. Knupp, K. T. 

Driscoll, and E. W. McCaul, 2000: The 1997-98 El 

Niño event and related wintertime lightning variations in the Southeastern United States. Geophys. 

Res. Lett., 27, 541-544.

Gordon, H. R., 1978: Removal of atmospheric effects 

from satellite imagery of the oceans. Appl. Opt., 17, 

1631-1636.

Gordon, H. R., 1987: Calibration requirements and 

methodology for remote sensors viewing the oceans 

in the visible. Remote Sens. Environ., 22, 103-126.

Gordon, H. R., 1997: Atmospheric correction of 

ocean color imagery in the Earth Observing System 

era. J. Geophys. Res., 102D, 17,081-17,106.

Gordon, H. R., and A. Y. Morel, 1983: Remote Assessment of Ocean Color for Interpretation of Satellite Visible Imagery: A Review, Springer-Verlag, 

New York, 114 pp.

Gordon, H. R., and D. K. Clark, 1981: Clear water 

radiances for atmospheric correction of coastal zone 

color scanner imagery. Appl. Opt., 20(24), 4175-

4180.

Gordon, H. R., O. B. Brown, R. H. Evans, J. W. 

Brown, R. C. Smith, K. S. Baker, and D. K. Clark, 

1988: A semi-analytic radiance model of ocean color. 

J. Geophys. Res., 93, 10,909-10,924.

Gordon, H. R., and M. Wang, 1994: Retrieval of 

water-leaving radiance and aerosol optical thickness over the oceans with SeaWiFS: A preliminary 

algorithm. Appl. Opt., 33, 443-452.

Gordon, H. R., T. Du, and T. Zhang, 1997: Remote 

sensing ocean color and aerosol properties: Resolving 

the issue of aerosol absorption. Appl. Opt., 36, 8670-

8684.

Goward, S. N., and A. S. Hope., 1989: Evapotranspiration from combined reflected solar and emitted 

terrestrial radiation: Preliminary FIFE results from 

AVHRR data. Adv. Space Res., 9(7), 239-249.

Goyet, C., and P. G. Brewer, 1993: Biochemical 

properties of the oceanic cycle. In Modeling Oceanic 

Climate Interactions, ed. by J. Willebrand, NATO 

Advanced Study Institute, I11, 271-297.

Green, R. N., and B. A. Wielicki, 1995: Convolution of imager cloud properties with CERES footprint point spread function (Subsystem 4.4). Clouds 

and the Earthʼs Radiant Energy System (CERES) 

Algorithm Theoretical Basis Document, Volume III: 

Cloud Analyses and Radiance Inversions (Subsystem 

4), NASA Ref. Pub. 1376, Vol. III, ed. by the CERES 

Science Team, 177-194.

Green, R. N., B. A. Wielicki, J. A. Coakley, L. L. 

Stowe, and P. OʼR. Hinton, 1995: CERES inversion 

to instantaneous TOA fluxes (Subsystem 4.5). Clouds 

and the Earthʼs Radiant Energy System (CERES) 

Algorithm Theoretical Basis Document, Volume III: 

Cloud Analyses and Radiance Inversions (Subsystem 

4), NASA Ref. Pub. 1376, Vol. III, ed. by the CERES 

Science Team, 195-206.

Green, R. O., and J. E. Conel, 1995: Movement of 

water vapor in the atmosphere measured by an imaging spectrometer at Rogers Dry Lake, CA. Proc. 

Summaries of the Fifth Annual JPL Airborne Earth 

Science Workshop, JPL Pub. 95-1, 1, 79–83.

Greenstone, R., and M. D. King, 1999: EOS Science 

Plan Executive Summary: The State of Science in the 

EOS Program, NASA Goddard Space Flight Center, 

Greenbelt, MD, 64 pp.
Appendix C: References Data Products Handbook - Volume 1 235

Gregg, W. W., and K. L. Carder, 1990: A simple solar 

irradiance model for cloudless maritime atmospheres.

Limn. Oceanog., 35(8), 1657-1675.

Groom, S. B., and P. M. Holligan, 1987: Remote 

sensing of coccolithophore blooms. Adv. Space 

Res., 7, 73-78.

Guenther, B., G. D. Godden, X. Xiong, E. J. Knight, 

S. Y. Qiu, H. Montgomery, M. M. Hopkins, M. G. 

Khayat, and Z. Hao, 1998: Prelaunch algorithm and 

data format for the Level 1B calibration products 

for the EOS AM-1 Moderate Resolution Imaging 

Spectroradiometer (MODIS). IEEE Trans. Geosci. 

Remote Sens., 36, 1142.

Guenther, B., X. Xiong, V. V. Salomonson, W. L. 

Barnes, and J. Young, 2002: On-orbit performance 

of the Earth Observing System Moderate Resolution 

Imaging Spectroradiometer; first year of data. Remote 

Sens. Environ., 83, 16-30.

Gustafson, G. B., R. G. Isaacs, R. P. dʼEntremont, 

J. M. Sparrow, T. M. Hamill, C. Grassotti, D. W. 

Johnson, C. P. Sarkisian, D. C. Peduzzi, B. T. Pearson, V. D. Jakabhazy, J. S. Belfiore, and A. S. Lisa, 

1994: Support of Environmental Requirements for 

Cloud Analysis and Archive (SERCAA): Algorithm 

descriptions. Phillips Laboratory Scientific Report 

No. 2, Hanscom Air Force Base, MA, 100 pp.

Gutman, G. G., 1998: On the use of long-term global 

data of land reflectances and vegetation. J. Geophys. 

Res., 104(D6), 6241-6256.

Haddad, Z., E. A. Smith, C. Kummerow, T. Iguchi, 

M. R. Farrar, S. L. Durden, M. Alves, and W. S. Olsen, 1997a: The TRMM “day-1” radar/radiometer 

combined rain-profiling algorithm. J. Meteor. Soc. 

Japan, 75, 799-809.

Haddad, Z. S., D. A. Short, S. L. Durden, E. Im, 

S. Hensley, M. B. Grable and R. A. Black, 1997b: 

A New Parametrization of the Rain Drop Size 

Distribution. IEEE Trans. Geosci. Rem. Sens., 35, 

532-539. 

Hall, D. K., R. E. J. Kelly, G. A. Riggs, A. T. C. 

Chang, and J. L. Foster, 2002: Assessment of the 

relative accuracy of hemispheric scale snow-cover 

maps. Ann. Glaciol., 34, 24-30.

Hall, D. K., G. A. Riggs, and V. V. Salomonson, 1995: 

Development of methods for mapping global snow 

cover using Moderate Resolution Imaging Spectroradiometer (MODIS) data. Remote Sens. Environ., 

54, 127-140.

Hall, D. K., G. A. Riggs, V. V. Salomonson, N. DiGirolamo, and K. Bayr, 2002a: MODIS Snow Products. 

Remote Sens. Environ., 83(1-2), 181-194.

Hansen, M. C., R. S. DeFries, J. R. G. Townshend, 

R. A. Sohlberg, C. Dimiceli, and M. Carroll, 2002b: 

Towards an operational MODIS continuous field 

of percent tree cover algorithm: Examples using 

AVHRR and MODIS data. Remote Sens. Environ., 

83(1-2), 303-319.

Harrison, E. F., D. R. Brooks, P. Minnis, B. A. 

Wielicki, W. F. Staylor, G. G. Gibson, D. F. Young, 

F. M. Denn, and the ERBE Science Team, 1988: 

First estimates of the diurnal variation of longwave 

radiation from the multiple-satellite Earth Radiation 

Budget Experiment (ERBE). Bull. Amer. Meteor. Soc., 

69, 1144-1151.

Harrison, E. F., P. Minnis, B. R. Barkstrom, B. A., 

Wielicki, G. G. Gibson, F. M. Denn, and D. F. Young, 

1990a: Seasonal variation of the diurnal cycles of 

Earthʼs radiation budget determined from ERBE. 

AMS 7th Conf. on Atmos. Radiation, San Francisco, 

CA, July 23-27, 87-91.

Harrison, E. F., P. Minnis, B. R. Barkstrom, V. Ramanathan, R. D. Cess, and G. G. Gibson, 1990b: 

Seasonal variation of cloud radiative forcing derived from the Earth Radiation Budget Experiment. 

J. Geophys. Res., 95, 18,687-18,703.

Harrison, E. F., D. F. Young, P. Minnis, G. G. Gibson, 

R. D. Cess, V. Ramanathan, T. D. Murray, and D. J. 

Travers, 1995: Clouds and the Earthʼs Radiant Energy System (CERES) Algorithm Theoretical Basis 

Document. Volume IV - Determination of Surface 

and Atmosphere Fluxes and Temporally and Spatially 

Averaged Products (Subsystems 5-12), Subsystem 

10.0 - Monthly Regional TOA and Surface Radiation 

Budget, NASA Ref. Pub. 1376, Vol. IV, 139-156.

Hayden, C. M., 1988: GOES-VAS simultaneous 

temperature-moisture retrieval algorithm. J. Appl. 

Meteor., 27, 705-733.

Hilland, J. E., D. B. Chelton, and E. G. Njoku, 1985: 

Production of global sea surface temperature fields for 

the Jet Propulsion Laboratory workshop comparisons. 

J. Geophys. Res., 90(C6), 11,642-11,650.

Hirano, A, R. Welch, and H. Lang, 2003: Mapping 

from ASTER stereo image data: DEM validation 

and accuracy assessment. ISPRS J. Photogram. 

Remote Sens., 57(5-6), 356-370.
236 Data Products Handbook - Volume 1 Appendix C: References 

Hobish, M. K., Ardanuy, P. E., and V. V. Salomonson, 1994: Surface imaging technologies for NASAʼs 

Earth Observing System. J. Imaging Science Tech., 

38, 301-310.

Hoffman, L. H., W. L. Weaver, and J. F. Kibler, 

1987: Calculation and accuracy of ERBE scanner 

measurement locations. NASA Tech. Paper 2670.

Hoge, F. E., and R. N. Swift, 1986: Chlorophyll 

pigment concentration using spectral curvature 

algorithms: An evaluation of present and proposed 

satellite ocean color sensor bands. Appl. Opt., 25, 

3677-3682.

Hoge, F. E., and R. N. Swift, 1990: Photosynthetic 

accessory pigments: Evidence for the influence of 

phycoerythrin on the submarine light field. Remote 

Sens. Environ., 34, 19-35.

Hoge, F. E., C. W. Wright, P. E. Lyon, R. N. Swift, 

and J. K. Yungel, 1999a: Satellite retrieval of inherent optical properties by inversion of an oceanic 

radiance model: A preliminary algorithm. Appl. Opt., 

38, 495-504.

Hoge, F. E., C. W. Wright, P. E. Lyon, R. N. Swift, 

and J. K. Yungel, 1999b: Satellite retrieval of the absorption coefficient of phytoplankton phycoerythrin 

pigment: Theory and feasibility status. Appl. Opt., 

38, 7431-7441.

Hoge, F. E., P. E. Lyon, R. N. Swift, J. K. Yungel, M. 

R. Abbott, R. M. Letelier, W. E Esaias, 2003: Validation of Terra-MODIS phytoplankton chlorophyll 

fluorescence line height. I. Initial airborne lidar 

results. Appl. Opt., 42(15), 2767-2771.

Holben, B. N., T. F. Eck, I. Slutsker, D. Tanré, J. 

P. Buis, A. Setzer, E. Vermote, J. A. Reagan, Y. J. 

Kaufman, T. Nakajima, F. Lavenu, I. Jankowiak, and 

A. Smirnov, 1998: AERONET-A federated instrument 

network and data archive for aerosol characterization. 

Remote Sens. Environ., 66(1), 1-16.

Holligan, P. M., E. Fernandez, J. Aiken, W. M. Balch, 

P. Boyd, P. H. Burkhill, M. Finch, S. B. Groom, G. 

Malin, K. Muller, D. A. Purdie, C. Robinson, C. C. 

Trees, S. M. Turner, and P. van der Wal, 1993: A biogeochemical study of the coccolithophore Emiliania 

huxleyi in the north Atlantic. Global Biogeochem. 

Cycles, 7, 879-900. 

Holligan, P. M., M. Viollier, D. S. Harbour, P. Camus, 

and M. Champagne-Philippe, 1983: Satellite and ship 

studies of coccolithophore production along the continental shelf edge. Nature, 304, 339-342.

Holm-Hansen, O., and B. Riemann, 1978: Chlorophyll a determination: Improvements in methodology. Oikos, 30, 438-477.

Holm-Hansen, O., C. J. Lorenzen, R. W. Holmes, and 

J. D. H. Strickland, 1965: Fluorometric determination 

of chlorophyll. J. Cons. Int. Explor. Mer., 30, 3-15.

Hong, Y., C. Kummerow, and W. S. Olson, 1999: 

Separation of Convective and Stratiform Precipitation Using Microwave Brightness Temperature. J. 

Appl. Meteor., 38(8), 1195-1213.

Hook, S. J., A. R. Gabell, A. A. Green, and P. S. Kealy, 1992: A comparison of techniques for extracting emissivity information from thermal infrared 

data for geological studies. Remote Sens. Environ., 

42, 123-135.

Horvath, A., and R. Davies, 2001: Feasibility and 

error analysis of cloud motion wind extraction from 

near simultaneous multiangle MISR measurements. 

J. Atmos. Oceanic Technol., 18, 591-608.

Horvath, A., and R. Davies, 2001: Simultaneous retrieval of cloud motion and height from polar-orbiter 

multiangle measurements. Geophys. Res. Lett., 28, 

2915-2918.

Hou, A. Y., D. V. Ledvina, A. M. Da Silva, S. Q. 

Zhang, J. Joiner, R. M. Atlas, G. J. Huffman, and C. 

Kummerow, 2000: Assimilation of SSM/I-derived 

surface rainfall and total precipitable water for improving the GEOS analysis for climate studies. Mon. 

Wea. Rev., 128, 509-537.

Houze, R. A. Jr., 1989: Observed structure of mesoscale convective systems and implications for 

large-scale heating. Quart. J. Roy. Meteor. Soc., 

115, 425-461.

Howard, K. L., and J. A. Yoder, 1997: Contribution 

of the subtropical ocean to global primary primary 

production. In Space Remote Sensing of the Subtropical Oceans, ed. by C. -T. Liu, Pergamon Press, 

New York, 157-168.

Hu, B., W. Lucht, and A. H. Strahler, 1999: The interrelationship of atmospheric correction of reflectances 

and surface BRDF retrieval: A sensitivity study. IEEE 

Trans. Geosci. Remote Sens., 37, 724-738.
Appendix C: References Data Products Handbook - Volume 1 237

Hu, B., W. Lucht, X. Li, and A. H. Strahler, 1997: Validation of kernel-driven models for global modeling 

of bidirectional reflectance. Remote Sens. Environ., 

62, 201-214.

Hu, B., W. Lucht, A. Strahler, C. Schaaf, and M. 

Smith, 2000: Surface albedos and angle-corrected 

NDVI from AVHRR observations over South America. Remote Sens. Environ., 71, 119-132.

Huete, A. R., K. Didan, T. Miura, and E. Rodriguez, 

2002: Overview of the Radiometric and Biophysical 

Performance of the MODIS Vegetation Indices. Remote Sens. Environ., 83(1-2), 195-213..

Huete, A. R., C. O. Justice, and W. van Leeuwen, 

1999: MODIS Vegetation Index (MOD 13) Algorithm 

Theoretical Basis Document (ATBD-MOD-13), 

version 3, available at eospso.gsfc.nasa.gov/atbd/

modistables.html.

Huete, A. R., C. Justice, and H. Liu, 1994: Development of vegetation and soil indices for MODISEOS. Remote Sens. Environ., 49, 224- 234.

Huete, A. R., H. Q. Liu, K. Batchily, and W. van 

Leeuwen, 1997: A comparison of vegetation indices 

over a global set of TM images for EOS-MODIS. 

Remote Sens. Environ., 59, 440-451.

Huffman, G. J., R. F. Adler, B. Rudolf, U. Schneider, 

and P. R. Keehn, 1995: A technique for combining 

satellite data, raingauge analysis and model precipitation information into global precipitation estimates. 

J. Climate, 8(5), 1284-1295.

Huffman, G. J., 1997a: Estimates of Root-MeanSquare Random Error for Finite Samples of Estimated Precipitation. J. Appl. Meteor., 36(9), 1191-

1201. 

Huffman, G. J., R. F. Adler, P. Arkin, A. Chang, 

R. Ferraro, A. Gruber, J. Janowiak, A. McNab, B. 

Rudolph, and U. Schneider, 1997b: The Global Precipitation Climatology Project (GPCP) Combined 

Precipitation Dataset. Bul. Amer. Meteor. Soc., 

78(1), 5-20. 

Ichoku, C., D. A. Chu, S. Mattoo, Y. J. Kaufman, L. A. 

Remer, D. Tanré, I. Slutsker, and B. N. Holben, 2002: 

A spatio-temporal approach for global validation and 

analysis of MODIS aerosol products. Geophys. Res. 

Lett., 29(12), 10.1029/2001GL013206.

Iguchi, T., T. Kozu, R. Meneghini, J. Awaka, and K. 

Okamoto, 1998: Preliminary results of rain profiling 

with TRMM Precipitation Radar. Proc. of URSI-F 

International Triennial Open Symposium on Wave 

Propagation and Remote Sensing, Aveiro, Portugal, 

147-150. 

Iguchi, T., T. Kozu, R. Meneghini, J. Awaka, and 

K. Okamoto, 2000: Rain-profiling algorithm for the 

TRMM Precipitation Radar. J. Appl. Meteor., 39, 

2038-2052.

Iguchi, T., and R. Meneghini, 1994: Intercomparison 

of single frequency methods for retrieving a vertical rain profile from airborne or spaceborne data. J. 

Atmos. Oceanic Technol., 11, 1507-1516.

Iguchi, T., T. Kozu, R. Meneghini, J. Awaka, and 

K. Okamoto, 2000: Rain profiling algorithm for the 

TRMM Precipitation Radar. J. Appl. Meteor., 39, 

2038-2052.

Inamdar, A. K., and V. Ramanathan, 1994: Physics of 

greenhouse effect and convection in warm oceans. J. 

Climate, 7, 715-731.

Inamdar, A. K., and V. Ramanathan, 1995: Clouds 

and the Earthʼs Radiant Energy System (CERES) 

Algorithm Theoretical Basis Document. Volume III 

- Cloud Analyses and Determination of Improved 

Top of Atmosphere Fluxes (Subsystem 4), Subsystem 

4.6.2 - Estimation of Longwave Surface Radiation 

Budget From CERES, NASA Ref. Pub. 1376, Vol. 

III, 217-234.

Inoue, T., 1987: An instantaneous delineation of 

convective rainfall areas using split window data 

of NOAA-7 AVHRR. J. Meteor. Res. Japan, 65, 

469-481.

Iqbal, M., 1983: An Introduction to Solar Radiation. 

Academic Press, Toronto and New York, 390 pp.

Iverson, R. L., W. E. Esaias, and K. R. Turpie, 2000: 

Ocean annual phytoplankton carbons and new production, and annual export production estimated with 

empirical equations and CZCS data. Global Change 

Biol., 6, 57-72.

Iwasaki, A., H. Fujisada, H. Akao, O. Shindou, and 

S. Akagi, 2002: Proc. Enhancement of spectral separation performance for ASTER/SWIR. In Infrared 

Spaceborne Remote Sensing IX, ed. by M. Strojnik 

and B. F. Andresen, SPIE Vol. 4486, 42-50.
238 Data Products Handbook - Volume 1 Appendix C: References 

Jarecke, P. J., M. A. Folkman, and L. A. Darnton, 

1991: Radiometric calibration plan for the clouds and 

the Earthʼs radiant energy system instruments. Proc. 

SPIE, 1493, 244-254.

Jovanovic, V. M., M. A. Bull, M. M. Smyth, and J. 

Zong, 2002: MISR in-flight camera geometric model 

calibration and georectification performance. IEEE 

Trans. Geosci. Remote Sens., 40, 1512-1519.

Jovanovic, V. M., M. M. Smyth, J. Zong, R. Ando, 

and G.W. Bothwell, 1998: MISR photogrammetric 

data reduction for geophysical retrievals. IEEE Trans. 

Geosci. Remote Sens., 36, 1290-1301.

Justice C. O., A. Belward, J. Morisette, P. Lewis, 

J. Privette, and F. Baret, 2000: Developments in 

the ʻvalidationʼ of satellite sensor products for the 

study of the land surface. Int. J. Remote Sens., 21, 

3383-3390.

Justice C. O., and S. A. Korontzi, 2001: A review 

of satellite fire monitoring and the requirements for 

global environmental change research. In Global and 

Regional Vegetation Fire Monitoring From Space: 

Planning a Coordinated International Effort, ed. by F. 

Ahern, G. Goldammer, C. O. Justice, SPB Academic 

Publishing, The Hague, The Netherlands, 1-18.

Justice C. O., L. Giglio, S. Korontzi, J. Owens, J. 

T. Morisette, D. Roy, J. Descloitres, S. Alleaume, F. 

Petitcolin, and Y. Kaufman, 2002: The MODIS Fire 

Products. Remote Sens. Environ., 83(1-2) 244-262.

Justice, C. O., J. P. Malingreau, and A. Setzer 1993: 

Satellite remote sensing of fires: potential and limitation. In Fire in the Environment: The ecological, 

atmospheric, and climatic importance of vegetation 

fires, ed. by P. J. Crutzen and J. G. Goldammer, John 

Wiley & Sons, Chichester, 77-88.

Justice, C. O., E. Vermote, J. R. G. Townshend, R. 

DeFries, D. P. Roy, D. K. Hall, V. V. Salomonson, 

J. L. Privette, G. Riggs, A. Strahler, W. Lucht, R. 

B. Myneni, Y. Knyazikhin, S. W. Running, R. R. 

Nemani, Z. Wan, A. R. Huete, W. van Leeuwen, R. 

E. Wolfe, L. Giglio, J.-P. Muller, P. Lewis, and M. J. 

Barnsley, 1998: The Moderate Resolution Imaging 

Spectroradiometer (MODIS): Land remote sensing 

for global change research. IEEE Trans. Geosci. 

Remote Sens., 36, 1228-1249.

Justice, C. O., R. Wolfe, N. El-Saleous, J. Descloitres, 

E. Vermote, D. Roy, J. Owens, E. Masuoka, and the 

MODIS Land Science Team (MODLAND), 2001: 

The Availability and Status of MODIS Land Data 

Products. The Earth Observer, 13, 16-17.

Justice C. O., J. R. G. Townshend, E. F. Vermote, E. 

Masuoka, R. E. Wolfe, N. El Saleous, D. P. Roy, and 

J. T. Morisette, 2002: An overview of MODIS Land 

data processing and product status. Remote Sens. 

Environ., 83(1-2), 3-15.

Kaab, A., 2002: Monitoring high-mountain terrain 

deformation from repeated air- and spaceborne optical data: examples using digital aerial imagery and 

ASTER data. ISPRS J. Photogram. Remote Sens., 

57(1-2), 39-52. 

Kahle, A. B., 1986: Surface emittance, temperature, 

and thermal inertia derived from Thermal Infrared 

Multispectral Scanner (TIMS) data for Death Valley, 

California. Geophysicas, 52(7), 858-874.

Kahle, A., F. Palluconi, S. Hook, V. J. Realmuto, and 

G. Bothwell, 1991: The Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER). 

Int. J. Imaging Syst. Technol., 3, 144-156.

Kahn, R., P. Banerjee, and D. McDonald, 2001: 

Sensitivity of multi-angle imaging to natural mixtures of aerosols over ocean. J. Geophys. Res., 106, 

18,219-18,238.

Kahn, R., P. Banerjee, D. McDonald, and D. J. Diner, 

1998: Sensitivity of multi-angle imaging to aerosol 

optical depth and to pure-particle size distribution 

and compositon over ocean. J. Geophys. Res., 103, 

32,195-32,213.

Kahn, R., P. Banerjee, D. McDonald, and J. Martonchik, 2001: Aerosol Properties Derived from Aircraft 

Multi-angle Imaging Over Monterey Bay. J. Geophys. 

Res., 106, 11,977-11,995. 

Kahn, R., R. West, D. McDonald, B. Rheingans, and 

M. I. Mishchenko, 1997: Sensitivity of multi-angle 

remote sensing observations to aerosol sphericity. J. 

Geophys. Res., 102, 16,861-16,870.

Kaufman, Y. J., R. S. Fraser, and R. L. Mahoney, 

1991: Fossil fuel and biomass burning effects on 

climate-heating or cooling? J. Climate, 4, 578-588.

Kaufman, Y. J., and B. C. Gao, 1992: Remote sensing of water vapor in the near IR from EOS/MODIS. 

IEEE Trans. Geosci. Remote Sens., 30, 871–884.
Appendix C: References Data Products Handbook - Volume 1 239

Kaufman, Y. J., C. O. Justice, L. P. Flynn, J. D. Kendall, E. M. Prins, L. Giglio, D. E. Ward, P. Menzel, and 

A. W. Setzer, 1998: Potential global fire monitoring 

from EOS-MODIS. J. Geophys. Res., 103, 31,955, 

32,215-32,238.

Kaufman, Y. J., and L. A. Remer, 1994: Remote 

sensing of vegetation in the mid-IR: The 3.75 μm 

channels. IEEE Trans. Geosci. Remote Sens., 32, 

672–683.

Kaufman, Y. J., D. Tanré, and O. Boucher, 2002: A 

satellite view of aerosols in the climate system. Nature, 419, 215-223.

Kaufman, Y. J., D. Tanré, H. R. Gordon, T. Nakajima, 

J. Lenoble, R. Frouin, H. Grassl, B. M. Herman, M. 

D. King, and P. M. Teillet, 1997a: Passive remote 

sensing of tropospheric aerosol and atmospheric 

correction for the aerosol effect. J. Geophys. Res., 

102(D14), 16,815-16,830. 

Kaufman, Y. J., D. Tanré, L. Remer, E. Vermote, A. 

Chu, and B. N. Holben, 1997b: Operational remote 

sensing of tropospheric aerosol over land from EOSmoderate resolution imaging spectroradiometer. J. 

Geophys. Res., 102(D14), 17,051-17,068.

Kaufman, Y. J., C. J. Tucker, and I. Fung, 1990: 

Remote sensing of biomass burning in the tropics. 

J. Geophys. Res., 95(D7), 9927-9939.

Kearns, E., J. A. Hanafin, R. H. Evans, P. J. Minnett, 

and O. B. Brown, 2000: An Independent Assessment 

of Pathfinder AVHRR Sea Surface Temperature Accuracy Using the Marine Atmosphere Enitted Radiance Interferomter (MAERI). Bull. Amer. Meteor. 

Soc., 81(7), 1525-1536.

Kedem, B., L. S. Chiu, G. R. North, 1990: Estimation of mean rate—Application to satellite-observations. J. Geophys. Res., 95(D2), 1965-1972.

Keenan, T. D., G. J. Holland, M. J. Manton, and J. 

Simpson, 1988: TRMM ground truth in a monsoon 

environment: Darwin, Australia. Aust. Meteor. Mag., 

36, 81-90.

Key, J. R., and J. B. Collins, 1997: High-latitude 

surface temperature estimates from thermal satellite 

data. Remote Sens. Environ., 61, 302-309.

Kiefer, D. A., and R. A. Reynolds, 1992: Advances 

in understanding phytoplankton fluorescence and 

photosynthesis. In Primary Productivity and Biogeochemical Cycles in the Sea, ed. by P. G. Falkowski and 

A. D. Woodhead, Plenum, New York, 155-174.

Kilpatrick, K. A., G. P. Podesta, and R. H. Evans, 

2001: Overview of the NOAA/NASA Pathfinder 

Algorithm for Sea Surface Temperature and Associated Matchup Database. J. Geophys. Res., 106, 

9179-9198.

King, M. D., ed., 1999: EOS Science Plan: The State 

of Science in the EOS Program, NASA Goddard 

Space Flight Center, Greenbelt, MD, 397 pp.

King, M. D., and R. Greenstone, 1999: 1999 EOS 

Reference Handbook: A Guide to NASAʼs Earth Science Enterprise and the Earth Observing System, 

NASA Goddard Space Flight Center, Greenbelt, 

MD, 361 pp.

King, M. D., Y. J. Kaufman, W. P. Menzel, and D. 

Tanré, 1992: Remote sensing of cloud, aerosol, and 

water vapor properties from the Moderate Resolution 

Imaging Spectroradiometer (MODIS). IEEE Trans. 

Geosci. Remote Sens., 30, 2-27.

King, M. D., W. P. Menzel, P. S. Grant, J. S. Myers, 

G. T. Arnold, S. E. Platnick, L. E. Gumley, S. C. Tsay, 

C. C. Moeller, M. Fitzgerald, K. S. Brown, and F. G. 

Osterwisch, 1996: Airborne scanning spectrometer 

for remote sensing of cloud, aerosol, water vapor 

and surface properties. J. Atmos. Oceanic Technol., 

13, 777-794.

King, M. D., W. P. Menzel, Y. J. Kaufman, D. Tanré, 

B. C. Gao, S. Platnick, S. A. Ackerman, L. A. Remer, R. Pincus, and P. A. Hubanks, 2003: Cloud and 

aerosol properties, precipitable water, and profiles of 

temperature and humidity from MODIS. IEEE Trans. 

Geosci. Remote Sens., 41(2), 442-458.

King, M. D., S. C. Tsay, S. A. Ackerman, and N. F. 

Larsen, 1998: Discriminating heavy aerosol, clouds, 

and fires during SCAR-B: Application of airborne 

multispectral MAS data. J. Geophys. Res., 103, 

31,989-32,000.

King, M. D., Y. J. Kaufman, D. Tanré, and T. Nakajima, 1999: Remote sensing of tropospheric aerosols 

from space: Past, present, and future. Bull. Amer. Meteor. Soc., 80, 2229-2260.
240 Data Products Handbook - Volume 1 Appendix C: References 

Kleepsies, T. J., and L. M. McMillan, 1984: Physical 

retrieval of precipitable water using the split window 

technique. Proc. Conf. on Satellite Meteorology/

Remote Sensing and Applications, Amer. Meteor. 

Soc., Boston, MA, 55-57.

Klein, A. G., D. K. Hall, and G. A. Riggs, 1998: 

Improving snow-cover mapping in forests through 

the use of a canopy reflectance model. Hydrological 

Processes, 12, 1723-1744.

Klein, A. G., and D. K. Hall, 2000: Snow albedo 

determination using the NASA MODIS instrument. 

Proc. 56th Annual Eastern Snow Conference, 2-4 

June 1999, Fredericton, New Brunswick, Canada, 

77-85.

Klein, A. G., and J. Stroeve, 2002: Development and 

validation of a snow albedo algorithm for the MODIS 

instrument. Annal. Glaciol., 34, 45-52.

Knap, W. H., and J. Oerlemans, 1996: The surface 

albedo of the Greenland ice sheet: satellite-derived 

and in situ measurements in the Søndre Strømfjord 

area during the 1991 melt season. J. Glaciol., 42(141), 

364-374.

Knyazikhin, Y., J. V. Martonchik, D. J. Diner, R. B. 

Myneni, M. M. Verstraete, B. Pinty, and N. Gobron, 

1998: Estimation of vegetation canopy leaf area index 

and fraction of absorbed photosynthetically active 

radiation from atmosphere-corrected MISR data. J. 

Geophys. Res., 103, 32,239-32,256.

Knyazikhin, Y., J. V., Martonchik, R. B. Myneni, D. 

J. Diner, and S. W. Running, 1998: Synergistic algorithm for estimating vegetation canopy leaf area index 

and fraction of absorbed photosynthetically active 

radiation from MODIS and MISR data. J. Geophys. 

Res., 103, 32,257-32,276.

Koshak, W. J., J. W. Bergstrom, M. F. Stewart, H. J. 

Christian, J. M. Hall, and R. J. Solakiewicz, 2000: 

Laboratory calibration of the Optical Transient 

Detector and Lightning Imaging Sensor. J. Atmos. 

Oceanic Technol., 17, 905-915.

Kozu, T., K. Nakamura, R. Meneghini, and W. C. 

Boncyk, 1991: Dual-parameter radar rainfall measurement from space: A test result from an aircraft 

experiment. IEEE Trans. Geosci. Remote Sens., 29, 

690-703.

Kozu T., T. Kawanishi, H. Kuroiwa, M. Kojima, K. 

Oikawa, H. Kumagai, K. Okamoto, M. Okumura, H. 

Nakatsuka and K. Nishikawa, 2001: Development 

of precipitation radar onboard the tropical rainfall 

measuring mission satellite. IEEE Geosci. Remote 

Sens., 39(1), 102-116.

Kozu, T., 1995: A generalized surface echo radar 

equation for down-looking pencil beam radar. IEICE Trans. Commun., E78-B, 1245-1248. 

Kozu, T., and T. Iguchi, 1996: A preliminary study 

of non-uniform beam filling correction for spaceborne radar rainfall measurement. IEICE Trans. 

Commun., E79-B, 763-769. 

Krajewski, W. F., 1987: Co-kriging of radar-rainfall 

and rain gage data. J. Geophys. Res., 92(D8), 9571-

9580.

Kratz, D. P., and Z. Li, 1995: Clouds and the Earthʼs 

Radiant Energy System (CERES) Algorithm Theoretical Basis Document. Volume III - Cloud Analyses 

and Determination of Improved Top of Atmosphere 

Fluxes (Subsystem 4), Subsystem 4.6.1 - Estimation 

of Longwave Surface Radiation Budget from CERES, 

NASA Ref. Pub. 1376, Vol. III, 213-216.

Kummerow, C., W. S. Olson and L. Giglio. 1996: A 

Simplified Scheme for Obtaining Precipitation and 

Vertical Hydrometeor Profiles from Passive Microwave Sensors. IEEE Trans. on Geosci. and Remote 

Sens., 34, 1213-1232. 

Kummerow, C., W. Barnes, T. Kozu, J. Shiue, and 

J. Simpson, 1998: The Tropical Rainfall Measuring 

Mission (TRMM) sensor package. J. Atmos. Oceanic 

Technol., 15, 809-816.

Kummerow, C., and L. Giglio, 1994: A passive microwave technique for estimating rainfall and vertical 

structure information from space, Part I: Algorithm 

description. J. Appl. Meteor., 33, 3-18.

Kummerow, C., Y. Hong, W. S. Olson, S. Yang, R. F. 

Adler, J. McCollum, R. Ferraro, G. Petty, D. B. Shin, 

and T. T. Wilheit, 2001: The evolution of the Goddard 

Profiling algorithm (GPROF) for rainfall estimation 

from passive microwave sensors. J. Appl. Meteor., 

40, 1801-1820.
Appendix C: References Data Products Handbook - Volume 1 241

Kummerow, C., J. Simpson, O. Thiele, W. Barnes, 

A. T. C. Chang, E. Stocker, R. F. Adler, A. Hou, R. 

Kakar, F. Wentz, P. Ashcroft, T. Kozu, Y. Hong, K. 

Okamoto, T. Iguchi, H. Kuroiwa, E. Im, Z. Haddad, 

G. Huffman, B. Ferrier, W. S. Olson, E. Zipser, E. 

A. Smith, T. T. Wilheit, G. North, T. Krishnamurti, 

and K. Nakamura, 2000: The status of the Tropical 

Rainfall Measuring Mission (TRMM) after two years 

in orbit. J. Appl. Meteor., 39, 1965-1982.

Lee, Z. P., K. L. Carder, T. G. Peacock, C. O. Davis, 

and J. L. Mueller, 1996: Method to derive ocean absorption coefficients from remote sensing reflectance. 

Appl. Opt., 35(3), 453-462.

Lee III, R. B., B. R. Barkstrom, G. L. Smith, J. E. 

Cooper, L. P. Kopia, and R. W. Lawrence, 1996: The 

Clouds and Earthʼs Radiant Energy System (CERES) 

sensors and preflight calibration plans. J. Atmos. Oceanic Technol., 12, 300-313.

Letelier, R. M., M. R. Abbott, and D. M. Karl, 

1997: Chlorophyll natural fluorescence response to 

upwelling events in the Southern Ocean. Geophys. 

Res. Lett., 24, 409-412.

Levine, J. S., ed., 1991: Global Biomass Burning, 

MIT Press, Cambridge, MA, 569 pp.

Li, J., and H.-L. Huang, 1999: Retrieval of atmospheric profiles from satellite sounder measurements 

by use of the discrepancy principle. Appl. Opt., 38(6), 

916-923.

Li, J., and K. Nakamura, 2002: Characteristics of the 

mirror image of precipitation observed by the TRMM 

Precipitation Radar. J. Atmos. Oceanic Technol., 19, 

145-158.

Li, J., W. Wolf, W. P. Menzel, W. Zhang, H.-L. Huang, 

and T. H. Achtor, 2000: Global soundings of the atmosphere from ATOVS measurements: The algorithm 

and validation. J. Appl. Meteorol., 39, 1248-1268.

Li, J., C. C. Schmidt, J. P. Nelson, T. J. Schmit, and 

W. P. Menzel, 2001: Estimation of total ozone from 

GOES sounder radiances with high temporal resolution. J. Atmos. Oceanic Technol., 157-168.

Li, X., A. H. Strahler, and C. E. Woodcock, 1995: A 

hybrid geometric optical-radiative transfer approach 

for modeling albedo and directional reflectance of 

discontinuous canopies. IEEE Trans. Geosci. Remote 

Sens., 33, 466-480.

Li, X., F. Gao, J. Wang, and A. H. Strahler, 2001: A 

priori knowledge accumulation and its application 

to linear BRDF model inversions. J. Geophys. Res., 

D106, 11,925-11,935.

Li, Z., and H. Leighton, 1993: Global climatologies 

of solar radiation budgets at the surface and in the 

atmosphere from 5 years of ERBE data. J. Geophys. 

Res., 98, 4919-4930.

Liao, L., R. Meneghini, and T. Iguchi, 2001: Comparison of rain rate and reflectivity factor derived 

from the TRMM Precipitation Radar and the WSR88D over the Melbourne, FL Site. J. Atmos. Oceanic 

Technol., 18, 1959-1974.

Liang, S., A. H. Strahler, and C. W. Walthall, 1999: 

Retrieval of land surface albedo from satellite observations: A simulation study. J. Appl. Meteorol., 38, 

712-725.

Liou, K.-N., 1992: Radiation and Cloud Processes 

in the Atmosphere, Oxford University Press, Oxford, 

UK, 487 pp.

Liu, A. K., Y. Zhao, W. E. Esaias, J. W. Campbell, and 

T. S. Moore, 2002: Ocean surface layer drift revealed 

by satellite data. Eos Trans. Amer. Geophys. Union, 

83(7), 61-64.

Loeb, N. G., and R. Davies,1997: Angular dependence of observed reflectances: a comparison with 

plane parallel theory. J. Geophys. Res., 102, 6865-

6881.

Loeb, N. G., S. Seiji Kato, and B. A. Wielicki: 2002: 

Defining top-of-the-atmosphere flux reference level 

for Earth radiation budget studies. J. Climate, 15(22), 

3301-3309.

Loeb, N. G, N. Manalo-Smith, S. Kato, W. F. Miller, 

S. K. Gupta, P. Minnis, and B. A. Wielicki, 2003: 

Angular distribution models for top-of-atmosphere 

radiative flux estimation from the CERES instrument on the TRMM satellite. Part I:Methodology. 

J. Appl. Meteor., 42, 240-265.

Lorenzen, C. J., and S. W. Jeffrey, 1980: Determination of chlorophyll in sea water, UNESCO Tech. 

Paper. Marine Science, No. 35, 20 pp.

Lucht, W., 1998: Expected retrieval accuracies of 

bidirectional reflectance and albedo from EOS-MODIS and MISR angular sampling. J. Geophys. Res., 

103, 8763-8778.
242 Data Products Handbook - Volume 1 Appendix C: References 

Lucht, W., and P. Lewis, 2000: Theoretical noise sensitivity of BRDF and albedo retrieval from the EOSMODIS and MISR sensors with respect to angular 

sampling. Int. J. Remote Sens., 21(1), 81-98.

Lucht, W., C. B. Schaaf, and A. H. Strahler, 2000: An 

algorithm for the retrieval of albedo from space using 

semiempirical BRDF models. IEEE Trans. Geosci. 

Remote Sens., 38(2), 977-998.

Lyu, C., and W. Barnes, 2003: Four Years of TRMM/

VIRS On-Orbit Calibration and Performance Using 

Lunar Models and Data from Terra/MODIS. J. Atmos. 

Oceanic Technol., 20(3), 333-347.

Lyu, C., W. L. Barnes, and R. A. Barnes, 2000: First 

results from the on-orbit calibration of the visible and 

infrared scanner for the tropical rainfall measuring 

mission. J. Atmos. Oceanic Technol., 17, 385-394.

Lyu, C., and W. Barnes, 2003: Four Years of TRMM/

VIRS On-Orbit Calibration and Performance Using 

Lunar Models and Data from Terra/MODIS. J. Atmos. 

Oceanic Technol., 20(3), 333-347.

Magnuson, J. J., D. M. Robertson, B. J. Benson, and 

11 other authors, 2000: Historical trends in lake and 

river ice cover in the Northern Hemisphere. Science, 

289, 1743-1746.

Martins, J. V., D. Tanré, L. A. Remer, Y. J. Kaufman, 

S. Mattoo, and R. Levy, 2002: MODIS Cloud screening for remote sensing of aerosol over oceans using spatial variability. Geophys. Res. Lett., 29(12), 

10.1029/2001GL013252.

Martonchik, J. V., C. J. Bruegge, and A. H. Strahler, 

2001: A review of reflectance nomenclature used in 

remote sensing. Remote Sens. Rev., 19, 9-20.

Martonchik, J. V., D. J. Diner, R. Kahn, M. M. Verstraete, B. Pinty, H. R. Gordon, and T. P. Ackerman, 

1998: Techniques for the retrieval of aerosol properties over land and ocean using multiangle imaging. 

IEEE Trans. Geosci. Remote Sens., 36, 1212-1227.

Martonchik, J. V., D. J. Diner, K. A. Crean, and M. 

A. Bull, 2002: Regional aerosol retrieval results 

from MISR. IEEE Trans. Geosci. Remote Sens., 40, 

1520-1531.

Martonchik, J. V., D. J. Diner, B. Pinty, M. M. 

Verstraete, R. B Myneni, Y. Knyazikhin, and H. R. 

Gordon, 1998: Determination of land and ocean reflective, radiative, and biophysical properties using 

multiangle imaging. IEEE Trans. Geosci. Remote 

Sens., 36, 1266-1281.

Marzoug, M., and P. Amayenc, 1994: A class of 

single- and dual-frequency algorithms for rain rate 

profiling from a spaceborne radar. Part I: Principle 

and tests from numerical simulations. J. Atmos. 

Oceanic Technol., 11, 1480-1506. 

Mather, P. M., 1999: Computer Processing of Remotely-Sensed Images, an Introduction, Second Edition. New York: John Wiley & Sons, 137-138.

Meneghini, R., and K. Nakamura, 1990: Range profiling of the rain rate by an airborne weather radar. 

Remote Sens. Environ., 31, 193-209. 

Meneghini, R., T. Iguchi, T. Kozu, L. Liao, K. Okamoto, J. A. Jones, and J. Kwiatkowski, 2000: Use of 

the surface reference technique for path attenuation 

estimates from the TRMM Precipitation Radar. J. 

Appl. Meteor., 39, 2053-2070.

Meneghini, R., and J. A. Jones, 1993: An approach to 

estimate areal rain-rate distribution from spaceborne 

radar by the use of multiple thresholds. J. Appl. Meteor., 32, 386-398.

Meneghini, R., J. A. Jones, T. Iguchi, K. Okamoto, 

and J. Kwiatkowski, 2001: Statistical Methods of 

Estimating Average Rainfall over Large Space–Timescales Using Data from the TRMM Precipitation 

Radar. J. Appl. Meteor., 40(3), 568–585.

Meneghini. R., and T. Kozu, 1990: Spaceborne 

Weather Radar, Artech House, Boston, MA, 197 

pp.

Meneghini, R., 1998: Application of a Threshold 

Method to Airborne–Spaceborne AttenuatingWavelength Radars for the Estimation of Space–

Time Rain-Rate Statistics. J. Appl. Meteor., 37(9), 

924–938. 

Minnis, P., and E. F. Harrison, 1984: Diurnal variability of regional cloud and clear-sky radiative parameters derived from GOES data, Part III: November 

1978 radiative parameters. J. Climate Appl. Meteor., 

23, 1033-1051.

Minnis, P., D. P. Kratz, J. A. Coakley, Jr., M. D. King, 

R. Arduini, D. P. Garber, P. W. Heck, S. Mayor, W. 

L. Smith, Jr., and D. F. Young, 1995: Cloud optical 

property retrieval (Subsystem 4.3). Clouds and the 

Earthʼs Radiant Energy System (CERES) Algorithm 

Theoretical Basis Document, Volume III: Cloud Analyses and Radiance Inversions (Subsystem 4), NASA 

Ref. Pub. 1376, Vol. III, ed. by the CERES Science 

Team, 135-176.
Appendix C: References Data Products Handbook - Volume 1 243

Minnis, P., D. P. Garber, D. F. Young, R. F. Arduini, 

and Y. Takano, 1998: Parameterization of reflectance 

and effective emittance for satellite remote sensing of 

cloud properties. J. Atmos. Sci., 55, 3313-3339.

Minnis, P., D. F. Young, B. A. Wielicki, P. W. Heck, 

S. Sun-Mack, and T. D. Murray, 1999: Cloud properties Derived from VIRS for CERES. Proc. AMS 

10th Conf. Atmos. Rad., Madison, WI, June 28-July 

2, 21-24.

Minnis, P., D. F. Young, B. A. Weilicki, S. Sun-Mack, 

Q. Z. Trepte, Y. Chen, P. W. Heck, and X. Dong, 2002: 

A global cloud database from VIRS and MODIS for 

CERES. Proc. SPIE 3rd Intl. Asia-Pacific Environ. 

Remote Sens. Symp. 2002: Remote Sens. of Atmosphere, Ocean, Environment, and Space, Hangzhou, 

China, October 23-27.

Monteith, J. L., 1972: Solar radiation and productivity in tropical ecosystems. J. Appl. Ecology, 9, 

747-766.

Moody, A., and C. E. Woodcock, 1995: The influence 

of scale and the spatial characteristics of landscapes 

on land-cover mapping using remote sensing. Landscape Ecology, 10, 363-379.

MOPITT Science Team, 1996: MOPITT Algorithm 

Theoretical Basis Document: Conversion of MOPITT 

Digital Counts into Calibrated Radiances in Carbon 

Monoxide and Methane Absorption Bands (Level 0 

to Level 1), available at eospso.gsfc.nasa.gov/atbd/

mopittables.html. 

MOPITT Science Team, 1996: MOPITT Algorithm 

Theoretical Basis Document: Retrieval of Carbon 

Monoxide Profiles and Column Amounts of Carbon 

Monoxide and Methane from MOPITT Radiances 

(Level 1 to Level 2), available at

eospso.gsfc.nasa.gov/atbd/mopittables.html. 

Morel, A., and J. M. Andre, 1991: Pigment distribution and primary production in the Western Mediterranean as derived and modeled from Coastal Zone 

Color Scanner observations. J. Geophys. Res., 96, 

12,685-12,698.

Morel, A., and B. Gentili, 1991: Diffuse reflectance 

of oceanic waters: its dependence on Sun angle as 

influenced by the molecular scattering contribution. 

Applied Opt., 30, 4427-4438.

Morel, A., and B. Gentili, 1993: Diffuse reflectance 

of oceanic waters: II Bidirectional aspects. Applied 

Opt., 32, 6864-6879.

Morel, A., and B. Gentili, 1996: Diffuse reflectance 

of oceanic waters: III. Implication of bidirectionality 

for the remote sensing problem. Applied Opt., 35, 

4850-4862.

Morel, A., K. J. Voss, and B. Gentili, 1995: Bidirectional reflectance of oceanic waters: A comparison 

of modeled and measured upward radiance fields. J. 

Geophys. Res., 100C, 13,143-13,150.

Moroney, C., R. Davies, and J-P. Muller, 2002: Operational retrieval of cloud-top heights using MISR data. 

IEEE Trans. Geosci. Remote Sens., 40, 1532-1540.

Muchoney. D., J. Borak, H. Chi, M. Friedl, J. Hodges, 

N. Morrow, and A. Strahler, 1999: Application of 

the MODIS global supervised classification model 

to vegetation and land cover mapping of Central 

America. Int. J. Remote Sens., 21, 1115-1138. 

Muchoney, D., A. Strahler, J. Hodges, and J. LoCastro, 1999: The IGBP DISCover Confidence Sites 

and the System for Terrestrial Ecosystem Parameterization: Tools for Validating Global Land Cover 

Data. Photogrammetric Engineering Remote Sens., 

65, 1061-1067. 

Mugnai, A., E. A. Smith, and G. J. Tripoli, 1993: 

Foundations for physical-statistical precipitation 

retrieval from passive microwave satellite measurements. Part II: Emission source and generalized 

weighting function propertiesof a time dependent 

cloud-radiation model. J. Appl. Meteor., 32, 17-39.

Muller, J. F., and G. P. Brasseur, 1995: IMAGES: A 

three-dimensional chemical transport model of the 

global troposphere. J. Geophys. Res., 100, 16,445-

16,490.

Myneni, R. B., S. Hoffman, Y. Knyazikhin, J. L. 

Privette, J. Glassy, Y. Tian, Y. Wang, X. Song,Y. 

Zhang, G. R. Smith, A. Lotsch, M. Friedl, J. T. Morisette, P. Votava, R. R. Nemani, and S. W. Running, 

2002: Global products of vegetation leaf area and 

fraction absorbed PAR from year one of MODIS data. 

Remote Sens. Environ., 83(1-2), 214-231.

Myneni, R. B., C. D. Keeling, C. J. Tucker, G. Asrar, 

and R. R. Nemani, 1997a: Increased plant growth in 

the northern high latitudes from 1981 to 1991. Nature, 

386, 698-702.

Myneni, R. B., R. R. Nemani, and S. W. Running, 

1997b: Estimation of global leaf area index and 

absorbed PAR using radiative transfer model. IEEE 

Trans. Geosci. Remote Sens., 35, 1380-1393.
244 Data Products Handbook - Volume 1 Appendix C: References 

Nakajima, T. Y., and T. Nakajima, 1995: Wide-area 

determination of cloud microphysical properties from 

NOAA AVHRR measurements for FIRE and ASTEX 

regions. J. Atmos. Sci., 52, 4043-4059.

Nemani, R. R., L. Pierce, S. Running, and S. Goward, 

1993: Developing satellite-derived estimates of surface moisture status. J. Appl. Meteor., 32(3), 548-

557.

Nemani, R. R., and S. W. Running, 1989: Estimation 

of regional surface resistance to evapotranspiration 

from NDVI and thermal-IR AVHRR data. J. Appl. 

Meteor., 28(4), 276-284.

Nemani, R. R., and S. W. Running, 1995: Satellite 

monitoring of global land-cover changes and their 

impact on climate. Climatic Change, 31, 395-413.

Neckel, H., and D. Labs, 1984: The solar radiation between 3300 and 12500 A. Solar Phys., 90, 205-258.

Nishida, K., R. R. Nemani, S. W. Running, and J. 

M. Glassy, 2003: An operational remote sensing 

algorithm of land surface evaporation. J. Geophys. 

Res.-Atmos., 108(D9), 4270.

Novelli, P. C., K. A. Masarie, P. P. Tans, and P. M. 

Lang, 1994: Recent changes in atmospheric carbon 

monoxide. Science, 263, 1587-1589.

Ogawa, K., T. Schmugge, F. Jacob, and A. French, 

2003: Estimation of land surface window (8-12 mμ 

m) emissivity from multispectral thermal infrared 

remote sensing—A case study in a part of Sahara 

Desert. Geophys, Res. Lett., 30(2), 1067.

Olson, W. S., C. D. Kummerow, Y. Hong, and W. 

Tao, 1999: Atmospheric Latent Heating Distributions in the Tropics Derived from Satellite Passive 

Microwave Radiometer Measurements. J. Appl. 

Meteor., 38(6), 633–664.

OʼNeill, M. A., and I. J. Dowman, 1993: A simulation 

study of the ASTER sensor using versatile general 

purpose rigid sensor modeling systems. Int. J. Remote 

Sens., 14, 565-585.

OʼReilly, J. J. and 21 co-authors, 2000: Ocean color 

chlorophyll a algorithms for SeaWiFS, OC2 and 

OC4: Version 4. Chapter 2 in SeaWiFS Post-launch 

Calibration and Validation Analyses, Part 3, SeaWiFS 

Post-launch Technical Memorandum Series, Vol. 11, 

NASA.

Pan, L., D. Edwards, J. Gille, M. W. Smith, and J. R. 

Drummond, 1995: Satellite remote sensing of tropospheric CO and CH4

: Forward model studies of the 

MOPITT instrument. Appl. Opt., 34, 6976-6988.

Pan, L., J. C. Gille, D. P. Edwards, P. L. Bailey, 

and C. D. Rodgers, 1998: Retrieval of tropospheric 

carbon monoxide for the MOPITT experiment. J. 

Geophys. Res., 103(32), 277-290. 

Panferov, O., Y. Knyazikhin, R. B. Myneni, J. 

Szarzynski, S. Engwald, K. G. Schnitzler, and G. 

Gravenhorst, 2001: The role of canopy structure in 

the spectral variation of transmission and absorption 

of solar radiation in vegetation canopies. IEEE Trans. 

Geosci. Remote Sens., 39(2), 241-253.

Parkinson, C. L., 1997: Earth from Above: Using 

Color-Coded Satellite Images to Examine the Global 

Environment, University Science Books, Sausalito, 

California, 176 pp.

Parkinson, C. L., 2002: Trends in the length of the 

Southern Ocean sea-ice season, 1979-99. Annal. 

Glaciol., 34, 435-440.

Penner, J. E., R. E. Dickinson, and C. A. OʼNeill, 

1992: Effects of aerosol from biomass burning on the 

global radiation budget. Science, 256, 1432-1434.

Petersen, W. A., and S. A. Rutledge, 2001: Regional 

variability in tropical convection: observations from 

TRMM. J. Climate, 14, 3566-3586.

Petitcollin F., and E. F. Vermote, 2002: Land surface 

reflectance, emissivity and temperature from MODIS 

middle and thermal infrared data. Remote Sens. Environ., 83(1-2), 112-134.

Pfaendtner, J., S. Bloom, D. Lamich, M. Seablom, M. 

Sienkiewicz, J. Stobie, and A. da Silva, 1995: Documentation of the Goddard Earth Observing System 

(GEOS) Data Assimilation System—Version 1. NASA 

Tech Memo. 104606, Vol. 4, NASA Goddard Space 

Flight Center, Greenbelt, MD, 44 pp.

Pinty, B., C. Leprieur, and M. M. Verstraete, 1993: 

Towards a quantitative interpretation of vegetation indices, part1: biophysical canopy properties and classical indices. Remote Sens. Environ., 7, 127-150.

Platnick, S., P. A. Durkee, K. Nielson, J. P. Taylor, 

S. C. Tsay, M. D. King, R. J. Ferek, P. V. Hobbs, and 

J. W. Rottman, 2000: The role of background cloud 

microphysics in the radiative formation of ship tracks. 

J. Atmos. Sci., 57, 2607-2624.
Appendix C: References Data Products Handbook - Volume 1 245

Platnick, S., M. D. King, S. A. Ackerman, W. P. 

Menzel, B. A. Baum, J. C. Riédi, and R. A. Frey, 

2003: The MODIS cloud products: Algorithms and 

examples from Terra. IEEE Trans. Geosci. Remote 

Sens., 41(2), 459-473.

Platt, T. C., C. Caverhill, and S. Sathyendranath, 

1991: Basin scale estimates of oceanic primary 

production by remote sensing: The North Atlantic. 

J. Geophys. Res., 96(15), 147-149.

Prabhakara, C., B. J. Conrath, and R. A. Hanel, 1970: 

Remote sensing of atmospheric ozone using the 9.6 

micron band. J. Atmos. Sci., 26, 689-697.

Priestley, K. J., B. R. Barkstrom, R. B. Lee III, R. 

N. Green, S. Thomas, R. S. Wilson, P. L. Spence, J. 

Paden, D. K. Pandey, and A. Al-Hajjah, 2000: Postlaunch radiometric validation of the Clouds and the 

Earthʼs Radiant Energy System (CERES) proto-flight 

model on the Tropical Rainfall Measuring Mission 

(TRMM) spacecraft through 1999. J. Appl. Meteor., 

39, 2249-2258.

Priestley, K. J., B. A. Wielicki, R. N. Green, M. P. 

A. Haeffelin, R. B. Lee and N. G. Loeb, 2002: Early 

radiometric validation results of the CERES flight 

model 1 and 2 instruments onboard NASAʼs Terra 

spacecraft. Adv. Space Res., 30(11), 2371-2376.

Price, J. C., 1983: Estimating surface temperature 

from satellite thermal infrared data—a simple formulation for the atmospheric effect. Remote Sens. 

Environ., 13, 353-361.

Price, J. C., 1984: Land surface temperature measurements from the split window channels of the NOAA 

7 Advanced Very High Resolution Radiometer. J. 

Geophys. Res., 89, 7231-7237.

Prince, S. D., and S. N. Goward, 1995: Global 

primary production: A remote sensing approach. J. 

Biogeography, 22, 815-835.

Ramanathan V., R. D. Cess, E. F. Harrison, P. Minnis, B. R. Barkstrom, E. Ahmad, and D. Hartmann, 

1989: Cloud-radiative forcing and climate: Results 

for the Earth Radiation Budget Experiment. Science, 

243, 57-63.

Ramsay, B., 1998: The interactive multisensor snow 

and ice mapping system. Hydrological Processes, 

12, 1537-1546.

Rao, C. R .N., L. L. Stowe, and E. P. McClain, 1989: 

Remote sensing of aerosols over the oceans using 

AVHRR data: Theory, practice, and applications. Int. 

J. Remote Sens., 10, 743-749.

Rasmusson, E. M., 1985: El Niño and variations in 

climate. Amer. Sci., 73, 168-178.

Reichle, Jr., H. G., H. A. Wallio, and B. E. Gormsen, 

1989: Feasibility of determining the vertical profile 

of carbon monoxide from a space platform. Appl. 

Opt., 28, 2104-2110.

Remer, L. A., Y. J. Kaufman, and B. N. Holben, 1996: 

The size distribution of ambient aerosol particles: 

Smoke vs. urban/industrial aerosol. In Global Biomass Burning, ed. by J. S. Levine, The MIT Press, 

Cambridge, MA, 519-530.

Remer, L. A., D. Tanré, Y. J. Kaufman, C. Ichoku, 

S. Mattoo, R. Levy, D. A. Chu, B. N. Holben, O. 

Dubovik, A. Smirnov, J. V. Martins, R.-R. Li, and 

Z. Ahmad, 2002: Validation of MODIS aerosol retrieval over ocean. Geophys. Res. Lett., 29(12), doi: 

10.1029/2001GL013204.

Riggs, G. A., and D. K. Hall, 2002: Proceedings of 

the 59th Eastern Snow Conference, 6-7 June 2002, 

Stowe, VT.

Riggs, G. A., D. K. Hall, and S. A. Ackerman, 1999: 

Sea Ice Extent and Classification Mapping with the 

Moderate Resolution Imaging Spectroradiometer 

Airborne Simulator. Remote Sens. Environ., 68, 

152-163.

Riggs, G. A., D. K. Hall, and V. V. Salomonson, 

2002a: MODIS Snow Products Userʼs Guide, modis-snow-ice.gsfc.nasa.gov/siugkc.html.

Riggs, G. A., D. K. Hall, and V.V. Salomonson, 

2002b: MODIS Sea Ice Products Userʼs Guide, modis-snow-ice.gsfc.nasa.gov/siugkc.html.

Robert III, L. B., B. R. Barkstrom, H. Bitting, D. A. H. 

Crommelynck, J. Paden, D. K. Pandey, K. J. Priestley, 

G. L. Smith, S. Thomas, K. L. Thornhill, and R. S. 

Wilson, 1998: Pre-Launch Calibrations of the CERES 

Tropical Rainfall Measuring Mission (TRMM) and 

Earth Observing System (EOS) Morning (AM-1) 

Spacecraft Thermistor Bolometers. IEEE Trans. 

Geosci. Remote Sens., 36(4), 1173-1185.
246 Data Products Handbook - Volume 1 Appendix C: References 

Robinson, J. M., 1991: Fire from space: Global fire 

evaluation using infrared remote sensing. Int. J. Remote Sens., 12(1), 3-24.

Roger J. C., and E. F. Vermote, 1997: Computation 

and use of the reflectivity at 3.75mm from AVHRR 

thermal channels. Remote Sens. Rev., 15, 75-98.

Roger J. C., and E. F. Vermote, 1998: A method to 

Retrieve the Reflectivity Signature at 3.75mm from 

AVHRR data. Remote Sens. Environ., 64, 103-114.

Rosenfeld, D., D. B. Wolff, and E. Amitai, 1994: 

The window probability matching method for rainfall measurements with radar. J. Appl. Meteor., 33, 

682-693.

Rossow, W. B., and L. C. Garder, 1993: Cloud 

detection using satellite measurements of infrared 

and visible radiances for ISCCP. J. Climate, 6, 

2341-2369.

Rothery, D. A., 1987: Decorrelation stretching as an 

aid to image interpretation. Int. J. Remote Sens., 8, 

1253-1254.

Roy, D. P., P. E. Lewis, and C. O. Justice, 2002: 

Burned area mapping using multi-temporal moderate 

spatial resolution data–a bi-directional reflectance 

model-based expectation approach. Remote Sens. 

Environ., 83(1-2), 263-286.

Roy, D. P., L. Giglio, J. K. Kendall, and C. Justice, 

1999: Multi-temporal active-fire based burn scar 

detection algorithm. Int. J. Remote Sens., 20(5), 

1031-1038.

Ruimy, A., B. Saugier, and G. Dedieu, 1994: Methodology for the estimation of terrestrial net primary 

production from remotely sensed data. J. Geophys. 

Res., 99(D3), 5263-5283.

Running, S. W., 1990: Estimating terrestrial primary 

productivity by combining remote sensing and ecosystem simulation. In Remote Sensing of Biosphere 

Functioning, ed. by R. Hobbs and H. Mooney, New 

York: Springer-Verlag, 65-86.

Running, S. W., C. Justice, V. Salmonson, D. Hall, 

J. Barker, Y. Kaufmann, A. Strahler, A. Huete, J.-

P. Muller, V. Vanderbilt, Z. Wan, P. Teillet, and D. 

Carneggie, 1994: Terrestrial remote sensing science 

and algorithms planned for EOS/MODIS. Int. J. 

Remote Sens., 15, 3587-3620.

Running, S. W., T. R. Loveland, L. L. Pierce, R. R. 

Nemani, and E. R. Hunt, 1995: A remote sensing 

based vegetation classification logic for global land 

cover analysis. Remote Sens. Environ., 51, 39-48.

Running, S. W., R. R. Nemani, D. L. Peterson, L. E. 

Band, D. F. Potts, L. L. Pierce, and M. A. Spanner, 

1989: Mapping regional forest evapotranspiration 

and photosynthesis by coupling satellite data with 

ecosystem simulation. Ecology, 70, 1090-1101.

Running, S. W., P. E. Thornton, R. R. Nemani, and 

J. M. Glassy, 2000: Global terrestrial gross and net 

primary productivity from the earth observing system. 

pp. 44-57. In Methods in Ecosystem Science, ed. by 

O. Sala, R. Jackson, and H. Mooney, Springer-Verlag, 

New York.

Salisbury, J. W., and D. M. DʼAria, 1992: Emissivity 

of terrestrial materials in the 8-14 mm atmospheric 

window. Remote Sens. Environ., 42, 83-106.

Sarmiento, J. L., J. R. Toggweiler, and R. Najjar, 

1988: Ocean carbon-cycle dynamics and atmospheric 

CO2. Philos. Trans. Roy. Soc. London, 325, 3-21.

Saunders, R. W., and K. T. Kriebel, 1988: An improved method for detecting clear-sky and cloud 

radiances for AVHRR data. Int. J. Remote Sens., 9, 

123-150.

Schols, J. L., and J. A. Weinman, 1994: Retrieval of 

hydrometeor distributions over the ocean from airborne single-frequency radar and multifrequency radiometric measurements. Atmos. Res., 34, 329-346.

Schubert, S. D., R. B. Rood, and J. Pfaendtner, 1993: 

An assimilated dataset for earth science applications. 

Bull. Amer. Meteor. Soc., 74, 2331-2342.

Sellers, P. J., 1987: Canopy reflectance, photosynthesis and transpiration. Int. J. Remote Sens., 6, 

1335-1372.

Seemann, S. W., J. Li, W. P. Menzel, and L. E. Gumley, 2003: Operational retrieval of atmospheric temperature, moisture and ozone from MODIS infrared 

radiances. J. Appl. Meteor., 42(8), 1072-1091.

Shapiro, M. A., A. J. Krueger, and P. J. Kennedy, 

1982: Nowcasting the position and intensity of jet 

streams using a satellite borne total ozone mapping 

spectrometer. In Nowcasting, ed. by K. A. Browning, 

Academic Press, London, UK, 137-145.
Appendix C: References Data Products Handbook - Volume 1 247

Short, D. A., K. Shimizu, and B. Kedem, 1993: Optimal Thresholds for the Estimation of Area RainRate Moments by the Threshold Method. J. Appl. 

Meteor., 32(2), 182–192. 

Simpson, J., C. Kummerow, W.-K. Tao, and R. F. 

Adler, 1996: On the Tropical Rainfall Measuring Mission (TRMM). Meteorol. Atmos. Phys., 60, 19-36.

Slater, P. N., S. F. Biggar, R. G. Holm, R. D. Jackson, 

Y. Mao, M. S. Moran, J. M. Palmer, and B. Yuan, 

1987: Reflectance-based and radiance-based methods 

for the in-flight absolute calibration of multi-spectral 

sensors. Remote Sens. Environ., 22, 11-37.

Smith, E. A., and T. D. Hollis, 2003: Performance 

evaluation of Level 2 TRMM Rain Profile algorithms 

by intercomparison and hypothesis testing. AMS 

Meteorological Monographs, Symposium in Cloud 

Systems, Hurricanes and TRMM (Special Issue for 

Joanne Simpson), in press.

Smith, E. A., C. Kummerow, and A. Mugnai: 1994a: 

The emergence of inversion-type profile algorithms 

for estimation of precipitation from satellite passive 

microwave measurements. Remote Sens. Rev., 11, 

211-242.

Smith, E. A., J. Turk, M. Farrar, A. Mugnai, and X. 

Xieng, 1997: Estimating 13.8 GHz path integrated attenuation from 10.7 GHz brightness temperatures for 

TRMM Combined PR-TMI Precipitation Algorithm. 

J. Appl. Meteor., 36, 365-388.

Smith, E. A., X. Xiang, A. Mugnai, and G. J. Tripoli, 

1994b: Design of an inversion-based precipitation 

profile retrieval algorithm using an explicit cloud 

model for initial guess microphysics. Meteor. Atmos. Phys., 54.

Smith, G. L., K. A. Bush, F. E. Martino, III, R. Hazra, 

N. Manalo-Smith, and D. Rutan, 1995: Clouds and 

the Earthʼs Radiant Energy System (CERES) Algorithm Theoretical Basis Document. Volume IV 

- Determination of Surface and Atmosphere Fluxes 

and Temporally and Spatially Averaged Products 

(Subsystems 5-12), Subsystem 9.0—Grid TOA and 

Surface Fluxes for Instantaneous Surface Product, 

NASA Ref. Pub. 1376, Vol. IV, 129-138.

Smith, G. L., R. N. Green, E. Raschke, L. M. Avis, 

J. T. Suttles, B. A. Wielicki, and R. Daview, 1986: 

Inversion methods for satellite studies of the Earthʼs 

radiation budget: Development of algorithms for the 

ERBE mission. Rev. Geophys., 24, 407-421.

Smith, M. W., and S. R. Shertz, 1996: Current plans 

and status of MOPITT algorithm test radiometer 

(MATR), in Earth Observing System , W. L. Barnes, 

ed., Proc. SPIE, 2820, 78-86.

Smith, R. C., and K. S. Baker, 1977: The bio-optical 

state of ocean waters and remote sensing. Scripps 

Institution of Oceanography, Ref. 77-2, 36 pp.

Smith, R. C., and K. S. Baker, 1982: Oceanic chlorophyll concentrations as determined by satellite 

(Nimbus-7 Coastal Zone Color Scanner). Marine 

Biology, 66, 269-279.

Smith, W. L., H. M. Woolf, and A. J. Schreiner, 1985: 

Simultaneous retrieval of surface and atmospheric parameters: A physical and analytically direct approach. 

In Advances in Remote Sensing Retrieval Methods, 

ed. by A. Deepak, H.E. Fleming, and M.T. Chahine, 

A. Deepak Publishing, Hampton, VA, 221-232.

Smith, W. L., and F. X. Zhou, 1982: Rapid extraction 

of layer relative humidity, geopotential thickness and 

atmospheric stability from satellite sounding radiometer data. Appl. Opt., 21, 924-928.

Snyder, W., and Z. Wan, 1996: Surface temperature 

correction for active infrared reflectance measurements of natural materials. Appl. Opt., 35(13), 

2216-2220.

Snyder, W., and Z. Wan, 1998: BRDF models to 

predict spectral reflectance and emissivity in the 

thermal infrared. IEEE Trans. Geosci. Remote Sens., 

36, 214-225.

Stajner, I., L. P. Riishojgaard, and R. B. Rood, 2001: 

The GEOS ozone data assimilation system: specification of error statistics. Quart. J. Roy. Meteor. Soc., 

127, 1069-1094.

Steele, L. P., E. J. Dlugokencky, P. M. Lang, P. P. Tans, 

R. C. Martin, and K. A. Masarie, 1992: Slowing down 

of the global accumulation of atmospheric methane 

during the 1980s. Nature, 358, 313-316.

Steiner, M., R. A. Houze, Jr., and S. E. Yuter, 1995: 

Climatological characterization of three-dimensional 

storm structure from operational radar and rain gauge 

data. J. Appl. Meteor., 34, 1978-2007.

Stowe, L. L., P. Ardanuy, R. Hucek, P. Abel, and H. 

Jacobowitz, 1993: Evaluating the design of an earth 

radiation budget instrument with system simulations. 

Part I: Instantaneous estimates. J. Atmos. Oceanic 

Tech., 10, 809-826.
248 Data Products Handbook - Volume 1 Appendix C: References 

Stowe, L. L., E. P. McClain, R. Carey, P. Pellegrino, 

G. Gutman, P. Davis, C. Long, and S. Hart, 1991: 

Global distribution of cloud cover derived from 

NOAA/AVHRR operational satellite data. Adv. 

Space Res., 11, 51-54.

Strabala, K. I., S. A. Ackerman, and W. P. Menzel, 

1994: Cloud properties inferred from 8-12 micron 

data. J. Appl. Meteor., 33, 212-229.

Strahler, A., A. Moody, and E. Lambin, 1995: Land 

cover and land-cover change from MODIS. Proc. 

15th Int. Geosci. and Remote Sens. Symp., Florence, 

Italy, July 10-14, 1995, vol. 2, 1535-1537.

Stroeve, J., A. Nolin, and K. Steffen, 1997: Comparison of AVHRR-derived and in situ surface albedo 

over the Greenland Ice Sheet. Remote Sens. Environ., 

62, 262-276.

Strugnell, N., and W. Lucht, 2001: Continental-scale 

albedo inferred from AVHRR data, land cover class 

and field observations of typical BRDFs. J. Climate, 

14, 1360-1376.

Strugnell, N., W. Lucht, and C. Schaaf, 2001: A 

global albedo data set derived from AVHRR data 

for use in climate simulations. Geophys. Res. Lett., 

28, 191-194.

Sullivan, J., L. Gandin, A. Gruber, and W. Baker, 

1993: Observation error statistics for NOAA-10 

temperature and height retrievals. Mon. Wea. Rev., 

121, 2578-2587.

Sun-Mack, S., Y. Chen, T. D. Murray, P. Minnis, and 

D. F. Young, 1999: Visible clear-sky and near-infrared 

surface albedos derived from VIRS for CERES. Proc. 

AMS 10th Conf. Atmos. Rad., Madison, WI, June 28-

July 2, 422-425.

Suttles, J. T., and G. Ohring, 1986: Surface radiation budget for climate applications. NASA Ref. Pub. 

1169, Washington, DC, 136 pp.

Takacs, L. L., A. Molod, and T. Wang, 1994: Documentation of the Goddard Earth Observing System 

(GEOS) General Circulation Model—Version 1. 

NASA Tech. Memo. 104606, Vol. 1, NASA Goddard 

Space Flight Center, Greenbelt, MD, 100 pp.

Tanré, D., F. M. Bréon, J. L. Deuzé, M. Herman, P. 

Goloub, F. Nadal, and A. Marchand, 2001: Global 

observation of anthropogenic aerosols from satellite. 

Geophys. Res. Lett., 28(24), 4555.

Tanré D., M. Herman, and Y. J. Kaufman, 1996: Information on the aerosol size distribution contained 

in the solar reflected spectral radiances. J. Geophys. 

Res., 101, 19,043-19,060.

Tanré, D., Y. J. Kaufman, M. Herman, and S. Mattoo, 1997: Remote sensing of aerosol properties over 

oceans using the MODIS/EOS spectral radiances. J. 

Geophys. Res., 102(D14), 16,971-16,988.

Tao, W.-K., S. Land, W. S. Olsen, S. Yang, R. 

Meneghini, J. Simpson, C. Kummerow, E. Smith, 

and J. Halverson, 2001: Retrieved vertical profiles 

of latent heating release using TRMM product for 

February 1998. J. Appl. Meteor., 40, 957-982.

Taylor, J. P., M. D. Glew, J. A. Coakley Jr., W. R. 

Tahnk, S. Platnick, P. V. Hobbs, and R. J. Ferek, 

2000: Effects of aerosols on the radiative properties 

of clouds. J. Atmos. Sci., 57(16), 2656-2670.

Theon, J. S., T. Matsuno, T. Sakata, and N. Fugono, 

Eds., 1992: The Global Role of Tropical Rainfall, A. 

Deepak Publishing, Hampton, VA, 280 pp.

Thomas, D., R. Vogel, W. E. Esaias, K. Turpie, and A. 

Bahatti, 2002: Status of MODIS Ocean Data. Powerpoint poster, EOS IWG meeting, Ellicott City, MD, 

November 18-20, 2002.

modis-ocean.gsfc.nasa.gov/refs.html

Thomas, R. J., P. R. Krehbiel, W. Rison, T. Hamlin, 

D. J. Boccippio, S. J. Goodman, and H. J. Christian, 

2000: Comparison of ground-based 3-dimensional 

lightning mapping observations with satellite-based 

LIS observations in Oklahoma. Geophys. Res. Lett., 

27, 1703-1706.

Thome, K., P. Slater, S. Arai, S. Hook, H. Lang, F. 

Palluconi, H. Kieffer, T. Matsunaga, A. Ono, and H. 

Sakuma, 1998a: ASTER preflight and inflight calibration and the validation of Level 2 products. IEEE 

Trans. Geosci. Remote Sens., 36(4), 1161-1172.

Thome, K., F. Palluconi, T. Takashima, and K. Masuda, 1998b: Atmospheric correction of ASTER. 

IEEE Trans. Geosci. Remote Sens., 36(4), 1199-

1211.

Tian, B., and V. Ramanathan, 2002: Role of tropical 

clouds in surface and atmospheric energy budget. J. 

Climate, 15(3), 296-305.
Appendix C: References Data Products Handbook - Volume 1 249

Tian, Y., Y. Wang, Y. Zhang, Y. Knyazikhin, J. 

Bogaert, and R. Myneni, 2003: Radiative transfer 

based scaling of LAI retrievals from reflectance 

data of different resolutions. Remote Sens. Environ., 

84(1), 143-159.

Tian, Y., Y. Zhang, Y. Knyazikhin, R. B. Myneni, 

J. M. Glassy, D. Dedieu, and S. W. Running, 2000: 

Prototyping of MODIS LAI and FPAR algorithm with 

LASUR and LANDSAT data. IEEE Trans. Geosci. 

Remote Sens., 38, 2387-2401.

Topliss, B. J., and T. Platt, 1986: Passive fluorescence 

and photosynthesis in the ocean: Implications for remote sensing. Deep Sea Res., 33, 849-864.

Toracinta, R. E., D. J. Cecil, E. J. Zipser, and S. W. 

Nesbitt, 2002: Radar, passive microwave, and lightning characteristics of precipitating systems in the 

Tropics. Mon. Wea. Rev., 130, 802-824.

Toutin, T., 2002: Three-dimensional topographic 

mapping with ASTER stereo data in rugged topography. IEEE Trans. Geosci. Remote Sens., 40(10), 

2241-2247.

Tovinkere, V. R., M. Penaloza, A. Logar, J. Lee, R. C. 

Weger, T. A. Berendes, and R. M. Welch, 1993: An 

intercomparison of artificial intelligence approaches 

for polar scene identification. J. Geophys. Res., 98, 

5001-5016.

Townshend, J. R. G., and C. O. Justice, 1988: Selecting the spatial resolution of satellite sensors required for global monitoring of land transformations. 

Int. J. Remote Sens., 9, 187-236.

Townshend, J. R. G., C. O. Justice, W. Li, C. Gurney, and J. McManus, 1991: Global land cover classification by remote sensing: Present capabilities 

and future possibilities. Remote Sens. Environ., 35, 

243-256.

Trepte, Q., Y. Chen, S. Sun-Mack, P. Minnis, D. F. 

Young, B. A. Baum, and P. W. Heck, 1999: Scene 

identification for the CERES cloud analysis subsystem. Proc. AMS 10th Conf. Atmos. Rad., Madison, 

WI, June 28-July 2, 169-172.

van Leeuwen, W. J. D., A. R. Huete, and T. W. Laing, 1999: MODIS vegetation index compositing 

approach: A prototype with AVHRR data. Remote 

Sens. Environ., 69, 264-280.

Vermote E. F., C. O. Justice, J. Descloitres, N. El Saleous, J. Ray, D. Roy, B. Margerin, and L. Gonzalez, 

2001: A global monthly coarse resolution reflectance 

data set from SeaWiFS for use in Land, Ocean and 

Atmosphere applications. Int. J. Remote Sens., 22(6), 

1151-1158.

Vermote, E. F., and D. P. Roy, 2002: Land Surface Hot-Spot observed by MODIS over Central 

Africa. Int. J. Remote Sens., Cover Letter (11), 

2141-2143. 

Vermote E. F., N. El Saleous, and C. O. Justice, 2002: 

Atmospheric correction of the MODIS data in the 

visible to middle infrared: First results. Remote Sens. 

Environ., 83(1-2), 97-111.

Vermote, E. F., N. El Saleous, C. O. Justice, Y. J. 

Kaufman, J. L. Privette, L. Remer, J. C. Roger, and 

D. Tanré, 1997: Atmospheric correction of visible to 

middle-infrared EOS-MODIS data over land surfaces: 

Background, operational algorithm and validation. J. 

Geophys. Res., 102(D14), 17,131-17,141.

Vermote E. F., N. El Saleous, Y. J. Kaufman, and E. 

Dutton, 1997: Stratospheric aerosol perturbing effect on the remote sensing of vegetation: Correction 

method for the composite NDVI after the Pinatubo 

eruption. Remote Sens. Rev., 15, 7-21.

Vermote E. F., D. Tanré, J. L. Deuzé, M. Herman, and 

J. J. Morcrette, 1997: Second simulation of the satellite signal in the solar spectrum: an overview. IEEE 

Trans. Geosci. Remote Sens., 35, 3,675-3,686.

Wan, Z., and J. Dozier, 1996: A generalized splitwindow algorithm for retrieving land-surface temperature from space. IEEE Trans. Geosci. Remote 

Sens., 34(4), 892-905.

Wan, Z., and Z.-L. Li, 1997: A physics-based algorithm for retrieving land-surface emissivity and 

temperature from EOS/MODIS data. IEEE Trans. 

Geosci. Remote Sens., 35, 980-996.

Wan, Z., Y. Zhang, Z.-L. Li, R. Wang, V. V. Salomonson, A. Yves, R. Bosseno, and J. F. Hanocq, 2002: 

Preliminary estimate of calibration of the Moderate 

Resolution Imaging Spectroradiometer (MODIS) 

thermal infrared data using Lake Titicaca. Remote 

Sens. Environ., 80, 497-515.
250 Data Products Handbook - Volume 1 Appendix C: References 

Wan, Z., Y. Zhang, Q. Zhang, and Z.-L. Li, 2002: 

Validation of the land-surface temperature products 

retrieved from Terra Moderate Resolution Imaging 

Spectroradiometer data. Remote Sens. Environ., 83, 

163-180.

Wang, Y., Y. Tian, Y. Zhang, N. El-Saleous, Y 

Knyazikhin, E. Vermote, and R. Myneni, 2001: 

Investigation of Product Accuracy as a Function 

of Input and Model Uncertainties: Case Study with 

SeaWIFS and MODIS LAI/FPAR Algorithm. Remote 

Sens. Environ., 78, 296-311.

Wang, J., J. C. Gille, P. L. Bailey, J. R. Drummond, 

and L. Pan, 1999: Instrument Sensitivity and Error 

Analysis for the Remote Sensing of Tropospheric 

Carbon Monoxide by MOPITT. J. Atmos. Oceanic 

Technol., 16, 465.

Wang, J., J. C. Gille, P. L. Bailey, L. Pan, D. Edwards, and J. R. Drummond, 1999: Retrieval of 

Tropospheric Carbon Monoxide Profiles from 

High-Resolution Interferometer Observations: A 

New Digital Gas Correlation (DGC) Method and 

Applications. J. Atmos. Sci., 56, 219.

Wang, J., J. C. Gille, and V. P. Walden, 2000: Validation Study of the MOPITT Retrieval Algorithm: 

Carbon Monoxide Retrieval from IMG Observations during WINCE. J. Atmos. Oceanic Technol., 

17, 1285. 

Wanner, W., X. Li, and A. H. Strahler, 1995: On 

the derivation of kernels for kernel-driven models 

of bidirectional reflectance. J. Geophys. Res., 100, 

21,077-21,090.

Wanner, W., A. H. Strahler, B. Hu, X. Li, C. L. Barker 

Schaaf, P. Lewis, J.-P. Muller, and M. J. Barnsley, 

1997: Global retrieval of bidirectional reflectance 

and albedo over land from EOS MODIS and MISR 

data: Theory and algorithm. J. Geophys. Res., 102, 

17,143-17,162.

Washington, W., and C. Parkinson, 1986: An Introduction to Three-Dimensional Climate Modeling. 

University Science Books, 422 pp.

Welch, R. M., K. S. Kuo, and S. L. Sengupta, 1990: 

Cloud and surface textural features in polar regions. 

IEEE Trans. Geosci. Remote Sens., 28, 520-528.

Welch, R. M., and H. Lang, 1994: ASTER as a source 

of global topographic data in the late 1990s. Int. Arch. 

Photogram. Remote Sens., 30, 222-224.

Welch, R. M., S. K. Sengupta, and D. W. Chen, 

1988: Cloud field classification based upon high 

spatial resolution textural features. 1. Gray level 

co-occurrence matrix approach. J. Geophys Res., 

93, 12,663-12,681.

Whitlock, C. H., T. P. Charlock, W. F. Staylor, R. T. 

Pinker, I. Laszlo, A. Ohmura, H. Gilgen, T. Konzelman, R. C. DiPasquale, C. D. Moats, S. R. LeCroy, 

and N. A. Ritchey, 1995: First global WCRP shortwave surface radiation budget dataset. Bull. Amer. 

Meteor. Soc., 76, 905-922.

Wielicki, B. A., B. R. Barkstrom, E. F. Harrison, 

R. B. Lee III, G. L. Smith, and J. E. Cooper, 1996: 

Clouds and the Earthʼs Radiant Energy System 

(CERES): An Earth Observing System Experiment. 

Bull. Amer. Met. Soc., 77, 853-868.

Wielicki, B. A., B. R. Barkstrom, and 21 others, 

1998: Clouds and the Earthʼs Radiant Energy System 

(CERES): Algorithm overview. IEEE Trans. Geosci. 

Remote Sens., 36, 1127-1141.

Wielicki, B. A., R. D. Cess, M. D. King, D. A. Randall, and E. F. Harrison, 1995: Mission to Planet 

Earth: Role of clouds and radiation in climate. Bull. 

Amer. Meteor. Soc., 76, 2125-2153.

Wilheit, T. T., A. T. C. Chang, and L. S. Chiu, 1991: 

Retrieval of monthly rainfall indices from microwave 

radiometric measurements using probability distribution functions. J. Atmos. Oceanic Technol., 8, 

118-136.

Wofsy, S. C., J. C. McConnell, and M. B. McElroy, 

1972: Atmospheric CH4, CO and CO2. J. Geophys. 

Res., 77, 4477-4493.

Wolfe R. E., M. Nishihama, A. J. Fleig, J. R. Kuyper, 

D. P. Roy, J. C. Storey, and F. S. Patt, 2002: Achieving sub-pixel geolocation accuracy in support of 

MODIS land science. Remote Sens. Environ., 83(1-

2), 31-49.

Wolfe, R. E., D. P. Roy, and E. F. Vermote, 1998: 

MODIS land data storage, gridding and compositing methodology: Level 2 grid. IEEE Trans. Geosci. 

Remote Sens., 36(4), 1324-1338.

Wong, T., D. Young, M. Haeffelin, and S. Weckman, 

2000: Validation of the CERES/TRMM ERBE-like 

monthly mean clear-sky longwave dataset and the 

effects of the 1998 ENSO event. J. Climate, 13(24), 

4256-4267.
Appendix C: References Data Products Handbook - Volume 1 251

Wu, M., and L.-P. Chang, 1992: Longwave radiation 

budget parameters computed from ISCCP and HIRS2/

MSU products. J. Geophy. Res., 97, 1083-1101.

Wu, M. L. C., S. Schubert, C.-I Lin, and I. Stajner, 

2001: A method for assessing the quality of modelbased estimates of ground temperature and atmospheric moisture using satellite data. J. Geophys. 

Res., 106, 10,129-10,144.

Wu, R., J. A. Weinman, and R. T. Chin, 1985: Determination of rainfall rates from GOES satellite images 

by a pattern recognition technique. J. Atmos. Oceanic 

Technol., 2, 314-330.

Wylie, D. P., and W. P. Menzel, 1998: Eight years of 

global high cloud statistics using HIRS. J. Climate, 

12, 170-184.

Yamaguchi, Y., H. Fujisada, A. B. Kahle, H. Tsu, M. 

Kato, H. Watanabe, I. Sato and M. Kudoh, 2001: ASTER instrument performance, operation status, and 

application to Earth sciences. IGARSS 2001, IEEE 

2001 International Geoscience and Remote Sensing Symposium, Volume 3, 9-13 July 2001, Sydney, 

Australia, pp. 1215-1216.

Yamaguchi, Y., A. Kahle, H. Tsu, T. Kawakami, and 

M. Pniel, 1998: Overview of ASTER. IEEE Trans. 

Geosci. Remote Sens., 36, 1062–1071.

Yamaguchi, Y., H. Tsu, and H. Fujisada, 1993: Scientific Basis of ASTER Instrument Design. Proc. SPIE, 

1939, 150-160.

Yamaguchi, Y., A. Tsu, A. Kahle, and D. A. Nochols, 1994: ASTER Instrument design and science 

Objectives. American Institute of Aeronomy and 

Astronomy, 7, paper 94-0597.

Yamaguchi, Y., H. Fujisada, H. Tsu, I. Sato, H. 

Watanabe, M. Kato, M. Kudoh, A. B. Kahle, and M. 

Pniel, 2001: ASTER early image evaluation —calibration and characterization of satellite sensors and 

accuracy of derived physical parameters. Adv. Space 

Res., 28(1), 69-76.

Yoder, J. A., C. R. McClain, G. C. Feldman, and W. 

E. Esaias, 1993: Annual cycles of phytoplankton chlorophyll concentrations in the global ocean, A satellite 

view. Global Biogeochem. Cycles, 7, 181-193.

Young, D. F., E. F. Harrison, B. A. Wielicki, P. Minnis, G. G. Gibson, B. R. Barkstrom, T. P. Charlock, 

D. R. Doelling, A. J. Miller, O. C. Smith, and J. C. 

Stassi, 1995a: Clouds and the Earthʼs Radiant Energy 

System (CERES) Algorithm Theoretical Basis Document. Volume IV - Determination of Surface and 

Atmosphere Fluxes and Temporally and Spatially 

Averaged Products (Subsystems 5-12), Subsystem 

7.0 - Time Interpolation and Synoptic Flux Computation for Single and Multiple Satellites, NASA Ref. 

Pub. 1376, Vol. IV, 69-108.

Young, D. F., E. F. Harrison, and E. Singh, 1995b: 

Clouds and the Earthʼs Radiant Energy System 

(CERES) Algorithm Theoretical Basis Document. Volume IV - Determination of Surface and 

Atmosphere Fluxes and Temporally and Spatially 

Averaged Products (Subsystems 5-12), Subsystem 

8.0 - Monthly Regional, Zonal, and Global Radiation 

Fluxes and Cloud Properties, NASA Ref. Pub. 1376, 

Vol. IV, 109-128.

Young, D. F., P. Minnis, D. Baumgardner, and H. 

Gerber, 1998: Comparison of in situ and satellitederived cloud properties during SUCCESS. Geophys. 

Res. Lett., 25, 1125-1128.

Yuter, S. E., and R. A. Houze, Jr. 1995: Three-dimensional kinematic and microphysical evolution of 

Florida cumulonimbus. Part II: Frequency distributions of vertical velocity, reflectivity and differential 

reflectivity. Mon. Wea. Rev., 123, 1941-1963.

Zander, R. P., Demoulin, D. H. Ehhalt, U. Schmidt, 

and C. P. Rinsland, 1989: Secular increase of the total vertical column abundance of carbon monoxide 

above central Europe since 1950. J. Geophy. Res., 

94, 11,021.

Zhan, X., R. DeFries, M. Hansen, C. DiMiceli, R. 

Sohlberg, and C. Huang. 1999: Algorithm Theoretical Basis Document of the MODIS Enhanced 

Land Cover and Land Cover Change Product (MOD 

29), update available at eospso.gsfc.nasa.gov/atbd/

modistables.html.

Zhan, X., R. DeFries, J. R. G. Townshend, C. DiMiceli, M. Hansen, C. Huang, and R. Sohlberg, 2000: 

The 250m global land cover change product from 

the Moderate Resolution Imaging Spectroradiometer 

of NASAʼs Earth Observing System. Int. J. Remote 

Sens., 21, 1433-1460.
252 Data Products Handbook - Volume 1 Appendix C: References

Zhan, X., R. A. Sohlberg, J. R. G. Townshend, 

C. DiMiceli, M. L. Carroll, J. C. Eastman, M. C. 

Hansen, and R. S. DeFries, 2002: Detection of land 

cover changes using MODIS 250 m data. Remote 

Sens. Environ., 83(1-2), 336-350.

Zhang, X., J. C. F. Hodges, C. Schaaf, M. A. Friedl, A. 

H. Strahler, and F. Gao, 2001: Global Vegetation Phenology from AVHRR and MODIS Data. Proc. 2001 

Int. Geosci. Remote Sens. Symp., Sydney, 2001.

Zhang, Y., Y. Tian, Y. Knyazikhin, J. V. Martonchik, 

D. J. Diner, M. Leroy, and R. B. Myneni, 2000: Prototyping of MODIS LAI and FPAR algorithm with 

POLDER data over Africa. IEEE Trans. Geosci. 

Remote Sens., 38, 2402-2418.
Appendix D: Acronyms and Abbreviations Data Products Handbook - Volume 1 253

A

ACRIM Active Cavity Radiometer Irradiance 

Monitor

ACRIMSAT ACRIM Satellite

ACP Aerosol Climatology Project

ADEOS Advanced Earth Observing Satellite

ADM Angular Distribution Model

AEM Applications Explorer Mission

AERONET Aerosol Robotic Network

AGC Automatic Gain Control

AGP Ancillary Geographic Product

AIRS Atmospheric Infrared Sounder

ALI Advanced Land Instrument

AMI Active Microwave Instrument

AMS American Meteorological Society

AMSR Advanced Microwave Scanning 

Radiometer

AMSR-E Advanced Microwave Scanning 

Radiometer for EOS

AMSU Advanced Microwave Sounding Unit

AOD Aerosol Optical Depth

APAR Absorbed Photosynthetically Active 

Radiation

APOLLO AVHRR Processing scheme Over Cloud 

Land and Ocean

AREAS Altimeter Return Echo Analysis System

ARM Atmospheric Radiation Measurement

ARMAR Airborne Rain Mapping Radar

ARP Absorbed Radiation by Phytoplankton

ARP Ancillary Radiometric Product (MISR)

ASAS Advanced Solid-state Array 

Spectroradiometer

ASF Alaska SAR Facility

ASTER Advanced Spaceborne Thermal Emission and Reflection Radiometer

ASTEX Atlantic Stratocumulus Transition 

Experiment

ATBD Algorithm Theoretical Basis Document

ATMS Advanced Technology Microwave 

Sounder

ATSR Along Track Scanning Radiometer

AVG Name used for the CERES average 

Monthly Regional Radiative Fluxes and 

Cloud product

AVHRR Advanced Very High 

Resolution Radiometer

AVIRIS Airborne Visible-Infrared Imaging 

Spectrometer

B

BDS Bi-Directional Scan

BGC Biogeochemical

BIOME Biogeochemical Information Ordering 

Management Environment

BOREAS Boreal Ecosystems Atmosphere Study

BRDF Bidirectional Reflectance Distribution 

Function

BUAN Baseline Upper Air Network

C

CO Carbon monoxide

CAGEX CERES/ARM/GEWEX Experiment

CART Cloud and Radiation Testbed

CCD Charge-Coupled Device

CCN Cloud Condensation Nuclei

CDDIS Crustal Dynamics Data Information 

System

CDOM Colored (or Chromomorphic) Dissolved 

Organic Matter

CEES Committee on Earth and Environmental 

Sciences

CERES Clouds and the Earthʼs Radiant Energy 

System

CFAD Contoured Frequency by Altitude 

Diagram

CFC Chlorofluorocarbon

CH4 Methane

CHEM-1 Chemistry Mission-1 (now Aura)

Appendix D: Acronyms and 

Abbreviations
254 Data Products Handbook - Volume 1 Appendix D: Acronyms and Abbreviations 

CrIS Cross-track Infrared Sounder

CLAVR Cloud Advanced Very high resolution 

Radiometer

ClOx Oxides of chlorine

CNES Centre National dʼEtudes Spatiales 

(France)

CO Carbon monoxide

CPF Calibration Parameter File

CRS Clouds and Radiative Swath

CZCS Coastal Zone Color Scanner

D

DAAC Distributed Active Archive Center

DAO Data Assimilation Office

DAS Data Assimilation System

DEM Digital Elevation Model

DIS Data and Information System

DMSP Defense Meteorological Satellite 

Program

DoE Department of Energy

DOM Dissolved Organic Matter

DORIS Doppler Orbitography and 

Radiopositioning Integrated by Satellite

DPH Data Products Handbook

DTED Digital Terrain Elevation Data

E

EASE Equal-Area Scalable Earth, a grid for 

data mapping

ECMWF European Centre for Medium-Range 

Weather Forecasts

ECS EOSDIS Core System

EDC EROS Data Center

EDST Earth Science Data Type

ENSO El Niño-Southern Oscillation

ENVISAT Environmental Satellite, ESA

EO-1 Earth Observing 1

EOS Earth Observing System

EOS PM EOS afternoon-crossing satellite (now 

Aqua)

EOSDIS EOS Data and Information System

EOSPSO EOS Project Science Office

EP Earth Probe

EPGN EOS Polar Ground Network

ERB Earth Radiation Budget

ERBE Earth Radiation Budget Experiment

ERBS Earth Radiation Budget Satellite

EROS Earth Resources Observation System

ERS European Remote Sensing Satellite

ERS-1 European Remote Sensing Satellite-1

ERTS Earth Resources Technology Satellite

ES EOS Science, a product identification 

code for CERES data products

ESA European Space Agency

ESDIS Earth Science Data and Information 

System

ESDT Earth Science Data Type

ESE Earth Science Enterprise

ESMR Electrically Scanning Microwave 

Radiometer

ESSP Earth System Sciences Pathfinder

ET Evapotranspiration

ETM Enhanced Thematic Mapper

ETM+ Enhanced Thematic Mapper Plus

EVI Enhanced Vegetation Index

F

FACE Florida Area Cumulus Experiment

FIFE First ISLSCP Field Experiment

FIRE First ISCCP Regional Experiment

FOV Field of View

FPAR Fraction of Photosynthetically Active 

Radiation

FSW Name used for the CERES Monthly 

Gridded Radiative Fluxes and Clouds 

product

FTP File Transfer Protocol

G

GARP Global Atmospheric Research Program

GATE GARP Atlantic Tropical Experiment

GCM General Circulation Model (also Global 

Climate Model)

GDR Geophysical Data Record

GEOS Goddard Earth Observing System

GEOS DAS Goddard Earth Observing System Data 

Assimilation System

GEOSAT Geodetic Satellite

GES DAAC Goddard Space Flight Center Earth 

Sciences Distributed Active Archive 

Center
Appendix D: Acronyms and Abbreviations Data Products Handbook - Volume 1 255

GEWEX Global Energy and Water cycle 

Experiment

GHRC Global Hydrology Resource Center

GOES Geostationary Operational 

Environmental Satellite

GPCP Global Precipitation Climatology 

Project

GPS Global Positioning System

GRACE Gravity Recovery And Climate 

Experiment

GRP Geo-rectified Radiance (product)

G/S Ground Station

GSFC Goddard Space Flight Center

H

HDF Hierarchical Data Format

HIRDLS High-Resolution Dynamics Limb 

Sounder

HIRS High-Resolution Infrared Radiation 

Sounder

HOX Hydrogen oxides

HRPT High-Resolution Picture Transmission

HSB Humidity Sounder for Brazil

I

ICEMAP Ice-Mapping Algorithm

ICESat Ice, Clouds, and Land Elevation 

Satellite

ID Identification

IEOS International Earth Observing System

IES Instrument Earth Scans

IFOV Instantaneous Field of View

IfSAR Interferometric Synthetic Aperture 

Radar

IGBP International Geosphere-Biosphere 

Programme

IGDR Interim Geophysical Data Record

IGS International Ground Station

IPAR Instantaneous Photosynthetically-Active 

Radiation

IPCC Intergovernmental Panel on Climate 

Change

IR Infrared

IR/MW Infrared/microwave

ISAMS Improved Stratospheric And 

Mesospheric Sounder

ISCCP International Satellite Cloud 

Climatology Project

ISLSCP International Satellite Land Surface 

Climatology Project

ITPP International TOVS Processing Package

J

JERS-1 Japanese Earth Resources Satellite

OPS Optical Sensor

JMR Jason-1 Microwave Radiometer

JPL Jet Propulsion Laboratory

K

Kbps Kilobits per second

L

LAI Leaf Area Index

Landsat Land Remote Sensing Satellite

LaRC Langley Research Center

LASE Laser Atmospheric Sensing Experiment

LI Lifted Index

LIS Lightning Imaging Sensor

LMC Length Modulation Cells

LP Level Processors

LST Land Surface Temperature

Ltyp Spectral radiance at typical conditions

LVIS Laser Vegetation Imaging Sensor

LW Longwave

M

MAPS Measurement of Air Pollution from 

Satellites

MAS MODIS Airborne Simulator

MB Megabyte

MBLA Multi-Beam Laser Altimeter

MERIS Medium Resolution Imaging 

Spectrometer

Met. data Meteorological data

METEOSAT Meteorology Satellite (an ESA 

geosynchronous satellite)

METI Ministry of Economy, Trade and 

Industry
256 Data Products Handbook - Volume 1 Appendix D: Acronyms and Abbreviations 

MISR Multi-angle Imaging SpectroRadiometer

MIT Massachusetts Institute of Technology

MLR Multiple Linear Regression

MLS Microwave Limb Sounder

MMS Multi-mission Modular Spacecraft

MOA Meteorological Ozone and Aerosol

MOBY Marine Optical Buoy

MODIS Moderate Resolution Imaging 

Spectroradiometer

MODLAND MODIS Land science team

MOLA Mars Orbiter Laser Altimeter

MOPITT Measurements of Pollution in the 

Troposphere

MSE Mean squared error

MSFC Marshall Space Flight Center

MSS Multispectral Scanner

MSU Microwave Sounding Unit

MVI Modified Vegetation Index

MW Microwave

MW/IR Microwave/infrared

MWP Microwave Water Path

N

N/A Not Applicable

NASA National Aeronautics and Space 

Administration

NASDA National Space Development Agency 

(Japan)

NBAR Nadir BRDF-Adjusted Reflectance

NCEP National Centers for Environmental 

Prediction

NCSA National Center for Supercomputing 

Applications (at the University of 

Illinois)

NDVI Normalized Difference Vegetation Index

NE Noise Equivalent

NESDIS National Environmental Satellite, Data, 

and Information Service

NGDC National Geophysical Data Center

NIR Near Infrared

NISN NASA Integrated Services Network

NMC National Meteorological Center

NMP New Millennium Program

NOAA National Oceanic and Atmospheric 

Administration

NOHRSC National Operational Hydrologic 

Remote Sensing Center

NORAD North American Aerospace Defense 

Command

NOX Nitrogen oxides (NO, NO2, NO3)

NPOESS National Polar-orbiting Operational 

Environmental Satellite System

NPP Net Primary Production

NSCAT NASA Scatterometer

NSIDC National Snow and Ice Data Center

NWS National Weather Service

O

OBC On-Board Calibrator

OClO Chlorine dioxide

OI Optimum Interpolation

OLR Outgoing Longwave Radiation

OLS Optical Linescan System

OMI Ozone Monitoring Instrument

ORNL Oak Ridge National Laboratory

OSDR Operational Sensor Data Record

OTD Optical Transient Detector

OX Oxides

P

PAR Photosynthetically-Active Radiation

PDF Portable Document Format

pdf Probability density function

PEB Phycoerythrobilin

PGE Product Generation Executive

PICASSO Pathfinder Instruments for Cloud and 

Aerosol Spaceborne Observations

PICASSO- Pathfinder Instruments for Cloud and 

CENA Aerosol Spaceborne Observations- Climatologie Etendue des Nuages et des Aerosols

PMC Pressure Modulation Cells

POCC Project Operation Control Center

PO.DAAC Physical Oceanography Distributed 

 Active Archive Center (at the Jet 

 Propulsion Laboratory) 

POES Polar Orbiting Environmental Satellite

POLDER Polarization and Directionality of the 

Earthʼs Reflectance

PP Pre-Processor

ppm Parts per million

ppmv Parts per million by volume

PR Precipitation Radar

PSC Polar Stratospheric Cloud

PSF Point Spread Functions
Appendix D: Acronyms and Abbreviations Data Products Handbook - Volume 1 257

PSN Photosynthesis

PUB Phycourobilin

Q

QA Quality Assurance

QuikScat Quick Scatterometer

QuikTOMS Quick Total Ozone Mapping 

Spectrometer

R

RAOBS Radiosonde Observations

RLRA Reflecting Level Reference Altitude

RMS Root Mean Square

RSMAS Rosenstiel School of Marine and 

Atmospheric Science, University of 

Miami

RSS Root Sum Square

S 

SAGE Stratospheric Aerosol and Gas 

Experiment

SAM Stratospheric Aerosol Measurement

SAMS Stratospheric And Mesospheric Sounder

SAR Synthetic Aperture Radar

SARB Surface and Atmospheric Radiation 

Budget

SASS SEASAT-A Satellite Scatterometer

SBRS Santa Barbara Remote Sensing

S/C Spacecraft

SCAR-B Smoke, Cloud, and Radiation-Brazil

SCF Science Computing Facility

SEASAT Sea Satellite

SeaWiFS Sea-viewing Wide Field-of-view Sensor

SEDAC Socioeconomic Data and Applications 

Center

SERCAA Support of Environmental Requirements 

for Cloud Analysis and Archive

SFC Surface Fluxes and Clouds

SGDR Sensor Geophysical Data Record

SGP Southern Great Plains

SI Systeme Internationale

SIM Spectral Irradiance Monitor

SIPS Science Investigator-led Processing 

System

SLA Shuttle Laser Altimeter

SLR Satellite Laser Ranging

SMM Solar Maximum Mission

SMMR Scanning Multichannel Microwave 

Radiometer

SNOMAP Snow-Mapping Algorithm 

SNR Signal-to-Noise Ratio

SOHO Solar Heliospheric Observatory

SOM Space-Oblique Mercator

SORCE Solar Radiation and Cloud Experiment

SPOT Systeme Pour lʼObservation de la Terre

SPOT-2 Systeme Pour lʼObservation de la Terre2

SRB Surface Radiation Budget

SRBAVG Name for CERES Monthly TOA/

Surface Averages product

SRTM Shuttle Radar Topography Mission

SSALTO Segment Sol Multimission Altimetry 

and Orbitography

SSF Single Scanner Footprint*

SSM/I Special Sensor Microwave/Imager

SST Sea Surface Temperature

SW Shortwave

SWE Snow Water Equivalent

SWH Significant Wave Height

SWIR Shortwave Infrared

SWS SeaWinds Standard

SYN Name for CERES Synoptic Radiative 

Fluxes and Clouds product

T

TARFOX Tropospheric Aerosol Radiative Forcing 

Observational Experiment

Tb Brightness temperature

TCP Topographic Control Points

TEC Total Electron Content

TES Tropospheric Emission Spectrometer

TIM Total Irradiance Monitor

TIMED Thermosphere, Ionosphere, Mesosphere, 

Energetics, and Dynamics

TIMS Thermal Infrared Multispectral Scanner 

TIR Thermal Infrared

TLSCF Team Leader Science Computing 

Facility

TM Thematic Mapper

TMI TRMM Microwave Imager

TMR TOPEX Microwave Radiometer

TOA Top Of the Atmosphere

*SSF is also used in the Product ID for the CERES Single Scanner TOA/Surface Fluxes and Clouds product
258 Data Products Handbook - Volume 1 Appendix D: Acronyms and Abbreviations

TOGA Tropical Ocean Global Atmosphere

TOMS Total Ozone Mapping Spectrometer

TOPEX Ocean Topography Experiment

TOPEX/ Ocean Topography Experiment/

Poseidon Poseidon (U.S.-France)

TOVS TIROS Operational Vertical Sounder

T/P TOPEX/Poseidon

TRMM Tropical Rainfall Measuring Mission

Ts Sea surface temperature

TSDIS TRMM Science Data and Information 

System

TSI Total Solar Irradiance

TSIM Total Solar Irradiance Mission

TT Total Totals

Ttyp Temperature at typical conditions

U

UARS Upper Atmosphere Research Satellite

UMD University of Maryland

UNESCO United Nations Educational, Scientific, 

and Cultural Organization

USGS United States Geological Survey

USNO United States Naval Observatory

UT Universal Time

UTC Universal Time Coordinate

UV Ultraviolet

V

VCL Vegetation Canopy Lidar

VDC VCL Data Center

VDIS Vertical Distribution of Intercepted 

Surfaces

VI Vegetation Index

VIIRS Visible Infrared Imaging Radiometer 

Suite

VIRGO Variability of solar Irradiance and 

Gravity Oscillations

VIRS Visible Infrared Scanner

VIS Visible

VISSR Visible Infrared Spin-Scan Radiometer

VNIR Visible and Near Infrared

VPGS VCL Precision Geolocation System

W

WCRP World Climate Research Program

WINCE Winter Cloud Experiment

WMO World Meteorological Organization

WN Window

Z

ZAVG Name for CERES Monthly Zonal and 

Global Radiative Fluxes and Clouds 

product
Appendix E: Data Products Index Data Products Handbook - Volume 1 259

ASTER (on Terra)

ASTER Brightness Temperature at Sensor 55

ASTER Browse Data-Decorrelation Stretch Product 56

ASTER Digital Elevation Models (DEMs) 59

ASTER Polar Surface and Cloud Classification

 Product 105

ASTER Reconstructed, Unprocessed Instrument 

 Data 54

ASTER Registered Radiance at Sensor 55

ASTER Surface Emissivity and Surface Kinetic

 Temperature 152

ASTER Surface Reflectance and Surface Radiance 57

CERES (on Terra)

CERES Bi-Directional Scans Product 61

CERES Clouds and Radiative Swath 111

CERES ERBE-like Instantaneous

 TOA Estimates 106

CERES ERBE-like Monthly

 Regional Averages and ERBE-like

 Monthly Geographical Averages 107

CERES Monthly Gridded Radiative

 Fluxes and Clouds 112

CERES Monthly Gridded TOA/Surface 

 Fluxes and Clouds 116

CERES Monthly Regional Radiative Fluxes

 and Clouds and Monthly Zonal 

 and Global Radiative Fluxes and Clouds 115

CERES Monthly TOA/Surface Averages 118

CERES Single Scanner TOA/

 Surface Fluxes and Clouds 109

CERES Synoptic Radiative Fluxes

 and Clouds 114

Data Assimilation System

DAS Instantaneous Near Surface and Vertically-

 Integrated State Variables 213

DAS Instantaneous 3-Dimensional State Variables 216

DAS Ozone Mixing Ratio 215

DAS Time-Averaged 3-Dimensional Temperature 

 Tendency Fields System 217

DAS Time-Averaged 3-Dimensional Eddy-Diffusivity 

 and Cloud Mass Flux Fields 218

DAS Time-Averaged Single Level Cloud

 Quantities 205

DAS Time-Averaged Near Surface and Vertically-

 Integrated Quantities 206

DAS Time-Averaged 2-Dimensional Surface Data 207

DAS Time-Averaged Surface and Top-of-the-

 Atmosphere Stresses 208

DAS Time-Averaged Surface 3-Dimensional Cloud 

 Quantities 209

DAS Time-Averaged 3-Dimensional Wind Tendency 

 Fields 210

DAS Time-Averaged 3-Dimensional Moisture Tendency 

 Fields 211

DAS Total Column Ozone 212

LIS (on TRMM)

LIS 2.5° × 2.5° Equal Angle Monthly Grid 140

LIS 500 km × 500 km Equal Area Monthly Grid 140

LIS Area Statistics 140

LIS Background Product 50

LIS Browse Area 140

LIS Ephemeris Data 140

LIS Event Rate Sets 140

LIS Events Statistics 140

LIS Flash Density 140

LIS Flash Rate Data 140

LIS Flash Statistics 140

LIS Group Statistics 140

LIS Image Attributes 140

LIS Metadata Description 140

LIS Orbit Attributes 140

LIS Summary Data 140

LIS Threshold Attributes 140

LIS Vector Statistics 140

MISR (on Terra)

MISR Aerosol and Surface Product 122

MISR Aerosol Climatology Product 123

MISR Ancillary Geographic Product 66

MISR Ancillary Radiometric Product 66

MISR Geo-rectified Radiance Product 65

MISR Radiometric Product 64

MISR Reformatted Annotated Product 63

MISR Top-of-Atmosphere (TOA)/Cloud Product 120

Appendix E: Data Products Index
260 Data Products Handbook - Volume 1 Appendix E: Data Products Index

MODIS (on Terra)

MODIS Absorption Coefficients 184

MODIS Aerosol Optical Depth 69

MODIS Aerosol Product 124

MODIS Atmospheric Profiles 98

MODIS Burn Scars 156

MODIS Chlorophyll a Pigment

 Concentration 184

MODIS Chlorophyll Fluorescence 182

MODIS Clear-Water Epsilon 73

MODIS Cloud Mask 134

MODIS Cloud Product 127

MODIS Coccolith Concentration 187

MODIS Evapotranspiration 169

MODIS Geolocation Data Set 68

MODIS Land Cover Type 164

MODIS Land Surface Temperature

 and Emissivity 154

MODIS Leaf Area Index and Fraction

 of Photosynthetically Active Radiation

 - Moderate Resolution 167

MODIS Level 1A Radiance Counts 67

MODIS Level 1B Calibrated, Geolocated Radiances 67

MODIS Level 3 Atmosphere Products 130

MODIS Normalized Water-Leaving Radiance 69

MODIS Ocean Primary Productivity 189

MODIS Ocean Water Attenuation Coefficient 180

MODIS Organic Matter Concentration 184

MODIS Photosynthetically Active Radiation 70

MODIS Phycoerythrin Concentration 190

MODIS Pigment Concentration 180

MODIS Processing Framework and 

 Match-up Database 72

MODIS Sea Ice Cover and Ice Surface

 Temperature 198

MODIS Sea Surface Temperature 157

MODIS Snow Cover 196

MODIS Surface Reflectance; Atmospheric

 Correction Algorithm Products 162

MODIS Surface Reflectance BRDF/

 Albedo Parameter 172

MODIS Suspended Solids Concentration 180

MODIS Thermal Anomalies – Fires 156

MODIS Total Precipitable Water 95

MODIS Vegetative Cover Conversion and Vegetation

 Continuous Fields 174

MODIS Vegetation Indices 166

MODIS Vegetation Production and

 Net Primary Production 170

MOPITT (on Terra)

MOPITT Geolocated Radiances 74

MOPITT CO Profile and CO Column, and CH4 Column

 Data 146

PR (on TRMM)

PR Radar Total Power, Noise, and Reflectivity 51

PR Rain Occurrence and Rain Type and 

 Bright Band 80

PR Surface Cross-Section as Function of 

 Scan Angle 79

PR Range Profiles of Rain and Water Content 81

PR Monthly Accumulated Rainfall & Vertical 

 Structure, Monthly Combined Accumulated 

 Surface Rainfall 82

TMI (on TRMM)

TMI Calibrated Brightness Temperatures 51

TMI Surface Rainfall and Vertical Structure 83

TMI Monthly Surface Rainfall 84

TRMM

TRMM Instantaneous Radar Site Rain Map 89

TRMM Instantaneous Radar Site Convective/

 Stratiform Map 90

TRMM Instantaneous Radar Site 3-D Reflectivities 91

TRMM Combined Surface Rainfall Rate and Vertical

 Structure 85

TRMM Monthly Combined Accumulated Rainfall and

 Vertical Structure 86

TRMM 1° Daily Combined Rainfall and Monthly 

 Combined Instrument Rainfall 88

TRMM 5-Day Site Rain Map 92

TRMM 30-Day Site Rain Map 93

TRMM Monthly 3-D Structure 93

VIRS (on TRMM)

VIRS Calibrated Radiances 52